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Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"]},"gpkg":{"type":"application/geopackage+sqlite3","roles":["metadata"]},"metadata":{"type":"application/xml","roles":["metadata"]},"thumbnail":{"type":"image/jpeg","roles":["thumbnail"]}},"stac_version":"1.0.0","msft:container":"3dep","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters","msft:region":"westeurope"},{"id":"3dep-lidar-dsm","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dsm","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Digital Surface Model","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dsm-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"},"geoparquet-items":{"href":"abfs://items/3dep-lidar-dsm.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","DSM"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[2.0]},"description":"This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","title":"COG data","raster:bands":[{"type":"Z","unit":"metre","sampling":"point","data_type":"float32","description":"Z PDAL Dimension"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json#","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for the Digital Surface Model (DSM) Cloud Optimized Geotiffs (COGs).","msft:region":"westeurope"},{"id":"fia","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia"},{"rel":"license","href":"https://www.fs.usda.gov/rds/archive/datauseinfo/open","type":"text/html","title":"USDA Open Access Data Use Agreement"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/fia","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Forest Inventory and Analysis","assets":{"guide":{"href":"https://www.fia.fs.fed.us/library/database-documentation/current/ver80/FIADB%20User%20Guide%20P2_8-0.pdf","type":"application/pdf","roles":["metadata"],"title":"Database Description and User Guide"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fia.png","type":"image/gif","title":"Forest Inventory and Analysis"},"geoparquet-items":{"href":"abfs://items/fia.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[138.06,0.92,163.05,9.78],[165.28,4.57,172.03,14.61],[131.13,2.95,134.73,8.1],[-124.763068,24.523096,-66.949895,49.384358],[-179.148909,51.214183,-129.974167,71.365162],[172.461667,51.357688,179.77847,53.01075],[-178.334698,18.910361,-154.806773,28.402123],[144.618068,13.234189,144.956712,13.654383],[-67.945404,17.88328,-65.220703,18.515683],[144.886331,14.110472,146.064818,20.553802],[-65.085452,17.673976,-64.564907,18.412655],[-171.089874,-14.548699,-168.1433,-11.046934],[-178.334698,18.910361,-154.806773,28.402123]]},"temporal":{"interval":[["2020-06-01T00:00:00Z",null]]}},"license":"CC0-1.0","keywords":["Forest","Species","Carbon","Biomass","USDA","Forest Service"],"providers":[{"url":"https://www.fia.fs.fed.us/","name":"Forest Inventory & Analysis","roles":["producer","licensor"]},{"url":"https://carbonplan.org/","name":"CarbonPlan","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's  [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928.  FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"cpdataeuwest"}}},"stac_version":"1.0.0","table:tables":[{"name":"Survey Table","description":"Survey table. This table contains one record for each year an inventory is conducted in a State for annual inventory or one record for each periodic inventory. <br><br>* SURVEY.CN = PLOT.SRV_CN links the unique inventory record for a State and year to the plot records.","msft:item_name":"survey"},{"name":"County Table","description":"County table. This table contains survey unit codes and is also a reference table for the county codes and names. <br><br>* COUNTY.CN = PLOT.CTY_CN links the unique county record to the plot record.","msft:item_name":"county"},{"name":"Plot Table","description":"Plot table. This table provides information relevant to the entire 1-acre field plot. This table links to most other tables, and the linkage is made using PLOT.CN = TABLE_NAME.PLT_CN (TABLE_NAME is the name of any table containing the column name PLT_CN). Below are some examples of linking PLOT to other tables. <br><br>* PLOT.CN = COND.PLT_CN links the unique plot record to the condition class record(s). <br><br>* PLOT.CN = SUBPLOT.PLT_CN links the unique plot record to the subplot records. <br><br>* PLOT.CN = TREE.PLT_CN links the unique plot record to the tree records. <br><br>* PLOT.CN = SEEDLING.PLT_CN links the unique plot record to the seedling records.","msft:item_name":"plot"},{"name":"Condition Table","description":"Condition table. This table provides information on the discrete combination of landscape attributes that define the condition (a condition will have the same land class, reserved status, owner group, forest type, stand-size class, regeneration status, and stand density). <br><br>* PLOT.CN = COND.PLT_CN links the condition class record(s) to the plot table. <br><br>* COND.PLT_CN = SITETREE.PLT_CN and COND.CONDID = SITETREE.CONDID links the condition class record to the site tree data. <br><br>* COND.PLT_CN = TREE.PLT_CN and COND.CONDID = TREE.CONDID links the condition class record to the tree data.","msft:item_name":"cond"},{"name":"Subplot Table","description":"Subplot table. This table describes the features of a single subplot. There are multiple subplots per 1-acre field plot and there can be multiple conditions sampled on each subplot. <br><br>* PLOT.CN = SUBPLOT.PLT_CN links the unique plot record to the subplot records. <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MACRCOND = COND.CONDID links the macroplot conditions to the condition class record. <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.SUBPCOND = COND.CONDID links the subplot conditions to the condition class record. <br><br>* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MICRCOND = COND.CONDID links the microplot conditions to the condition class record.","msft:item_name":"subplot"},{"name":"Subplot Condition Table","description":"Subplot condition table. This table contains information about the proportion of a subplot in a condition. <br><br>* PLOT.CN = SUBP_COND.PLT_CN links the subplot condition class record to the plot table. <br><br>* SUBP_COND.PLT_CN = COND.PLT_CN and SUBP_COND.CONDID = COND.CONDID links the condition class records found on the four subplots to the subplot description.","msft:item_name":"subp_cond"},{"name":"boundary","description":"Boundary table. This table provides a description of the demarcation line between two conditions that occur on a single subplot","msft:item_name":"boundary"},{"name":"Subplot Condition Change Matrix","description":"Subplot condition change matrix table. This table contains information about the mix of current and previous conditions that occupy the same area on the subplot. <br><br>* PLOT.CN = SUBP_COND_CHNG_MTRX.PLT_CN links the subplot condition change matrix records to the unique plot record. <br><br>* PLOT.PREV_PLT_CN = SUBP_COND_CHNG_MTRX.PREV_PLT_CN links the subplot condition change matrix records to the unique previous plot record.","msft:item_name":"subp_cond_chng_mtrx"},{"name":"Tree Table","description":"Tree table. This table provides information for each tree 1 inch in diameter and larger found on a microplot, subplot, or core optional macroplot. <br><br>* PLOT.CN = TREE.PLT_CN links the tree records to the unique plot record. <br><br>* COND.PLT_CN = TREE.PLT_CN and COND.CONDID = TREE.CONDID links the tree records to the unique condition record.","msft:item_name":"tree"},{"name":"Tree Woodland Stems Table","description":"Tree woodland stems table. This table stores data for the individual stems of a woodland species tree. Individual woodland stem diameter measurements contribute to the calculation of the diameter stored on the parent TREE table record. <br><br>* TREE.CN = TREE_WOODLAND_STEMS.TRE_CN links a woodland stems record to the corresponding unique tree record.","msft:item_name":"tree_woodland_stems"},{"name":"Tree Regional Biomass Table","description":"Tree regional biomass table. This table contains biomass estimates computed using equations and methodology that varies by FIA work unit. This table retains valuable information for generating biomass estimates that match earlier published reports. <br><br>* TREE.CN = TREE_REGIONAL_BIOMASS.TRE_CN links a tree regional biomass record to the corresponding unique tree.","msft:item_name":"tree_regional_biomass"},{"name":"Tree Net Growth, Removal, and Mortality Component Table","description":"Tree net growth, removal, and mortality component table. This table stores information used to compute net growth, removals, and mortality estimates for remeasurement trees. Each remeasurement tree has a single record in this table. <br><br>* TREE_GRM_COMPONENT.TRE_CN = TREE.TRE_CN links the records in this table to the corresponding tree record in the TREE table.","msft:item_name":"tree_grm_component"},{"name":"Tree Net Growth, Removal, and Mortality Midpoint Table","description":"Tree net growth, removal, and mortality midpoint table. This table contains information about a remeasured tree at the midpoint of the remeasurement period. It does not contain a record for every tree. Midpoint estimates are computed for trees that experience mortality, removal, or land use diversion or reversion. The information in this table is used to compute net growth, removal, and mortality estimates on remeasurement trees. <br><br>* TREE_GRM_MIDPT.TRE_CN = TREE.TRE_CN links the records in this table to the corresponding tree record in the TREE table.","msft:item_name":"tree_grm_midpt"},{"name":"Tree Net Growth, Removal, and Mortality Begin Table","description":"Tree net growth, removal, and mortality begin table. This table contains information for remeasured trees where values have been calculated for the beginning of the remeasurement period. Only those trees where information was recalculated for time 1 (T1) are included. The information in this table is used to produce net growth, removal and mortality estimates on remeasured trees. <br><br>* TREE_GRM_BEGIN.TRE_CN = TREE.TRE_CN links the records in this table to the corresponding tree record in the TREE table.","msft:item_name":"tree_grm_begin"},{"name":"Tree Net Growth, Removal, and Mortality Estimation Table","description":"Tree net growth, removal, and mortality estimation table. This table contains information used to produce estimates of growth, removals and mortality. <br><br>* PLOT.CN = TREE_GRM_ESTN.PLT_CN links the tree GRM estimation records to the unique plot record. <br><br>* TREE.CN = TREE_GRM_ESTN.TRE_CN links the tree GRM estimation records to the unique tree record.","msft:item_name":"tree_grm_estn"},{"name":"Seedling Table","description":"Seedling table. This table provides a count of the number of live trees of a species found on a microplot that are less than 1 inch in diameter but at least 6 inches in length for conifer species or at least 12 inches in length for hardwood species. <br><br>* PLOT.CN = SEEDLING.PLT_CN links the seedling records to the unique plot record. <br><br>* COND.PLT_CN = SEEDLING.PLT_CN and COND.CONDID = SEEDLING.CONDID links the condition record to the seedling record.","msft:item_name":"seedling"},{"name":"Site Tree Table","description":"Site tree table. This table provides information on the site tree(s) collected in order to calculate site index and/or site productivity information for a condition. <br><br>* PLOT.CN = SITETREE.PLT_CN links the site tree records to the unique plot record. <br><br>* SITETREE.PLT_CN = COND.PLT_CN and SITETREE.CONDID = COND.CONDID links the site tree record(s) to the unique condition class record.","msft:item_name":"sitetree"},{"name":"Invasive Subplot Species Table","description":"Invasive subplot species table. This table provides percent cover data of invasive species identified on the subplot. <br><br>* PLOT.CN = INVASIVE_SUBPLOT_SPP.PLT_CN links the invasive subplot species record(s) to the unique plot record. <br><br>* SUBP_COND.PLT_CN = INVASIVE_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = INVASIVE_SUBPLOT_SPP.CONDID and SUBP_COND.SUBP = INVASIVE_SUBPLOT_SPP.SUBP links the invasive subplot species record(s) to the unique subplot condition record. <br><br>* INVASIVE_SUBPLOT_SPP.VEG_SPCD = REF_PLANT_DICTIONARY.SYMBOL links the invasive vegetation subplot NRCS species code to the plant dictionary reference species code.","msft:item_name":"invasive_subplot_spp"},{"name":"Phase 2 Vegetation Subplot Species Table","description":"Phase 2 Vegetation subplot species table. This table provides percent cover data of vegetation species identified on the subplot. <br><br>* PLOT.CN = P2VEG_SUBPLOT_SPP.PLT_CN links the vegetation subplot species record(s) to the unique plot record. <br><br>* SUBP_COND.PLT_CN = P2VEG_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = P2VEG_SUBPLOT_SPP.CONDID and SUBP_COND.SUBP = P2VEG_SUBPLOT_SPP.SUBP links the vegetation subplot species record(s) to the unique subplot condition record. <br><br>* P2VEG_SUBPLOT_SPP.VEG_SPCD = REF_PLANT_DICTIONARY.SYMBOL links the P2 vegetation subplot NRCS species code to the plant dictionary reference species code.","msft:item_name":"p2veg_subplot_spp"},{"name":"Phase 2 Vegetation Subplot Structure Table","description":"Phase 2 Vegetation subplot structure table. This table provides percent cover by layer by growth habit. <br><br>* PLOT.CN = P2VEG_SUBP_STRUCTURE. PLT_CN links the subplot structure record(s) to the unique plot record. <br><br>* SUBP_COND.PLT_CN = P2VEG_SUBP_STRUCTURE.PLT_CN and SUBP_COND.CONDID = P2VEG_SUBP_STRUCTURE.CONDID and SUBP_COND.SUBP = P2VEG_SUBP_STRUCTURE.SUBP links the vegetation subplot structure record(s) to the unique subplot condition record.","msft:item_name":"p2veg_subp_structure"},{"name":"Down Woody Material Visit Table","description":"Down woody material visit table. This table provides general information on down woody material indicator visit, such as the date of the DWM survey. <br><br>* PLOT.CN = DWM_VISIT.PLT_CN links the down woody material indicator visit record to the unique plot record.","msft:item_name":"dwm_visit"},{"name":"Down Woody Material Coarse Woody Debris Table","description":"Down woody material coarse woody debris table. This table provides information for each piece of coarse woody debris measured along the transects. <br><br>* PLOT.CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN links the down woody material coarse woody debris records to the unique plot record. <br><br>* COND.PLT_CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN and COND.CONDID= DWM_COARSE_WOODY_DEBRIS.CONDID links the coarse woody debris records to the unique condition record.","msft:item_name":"dwm_coarse_woody_debris"},{"name":"Down Woody Material Duff, Litter, Fuel Table","description":"Down woody material duff, litter, fuel table. This table provides information on the duff, litter, fuelbed depths measured at a point on the transects. <br><br>* PLOT.CN = DWM_DUFF_LITTER_FUEL.PLT_CN links the duff, litter, fuelbed records to the unique plot record. <br><br>* COND.PLT_CN = DWM_DUFF_LITTER_FUEL.PLT_CN and COND.CONDID= DWM_DUFF_LITTER_FUEL.CONDID links the duff, litter, fuel records to the unique condition record.","msft:item_name":"dwm_duff_litter_fuel"},{"name":"Down Woody Material Fine Woody Debris Table","description":"Down woody material fine woody debris table. This table provides information on the fine woody debris measured along a segment of the transects. <br><br>* PLOT.CN = DWM_FINE_WOODY_DEBRIS.PLT_CN links the fine woody debris records to the unique plot record. <br><br>* COND.PLT_CN = DWM_FINE_WOODY_DEBRIS.PLT_CN and COND.CONDID= DWM_FINE_WOODY_DEBRIS.CONDID links the fine woody debris records to the unique condition record.","msft:item_name":"dwm_fine_woody_debris"},{"name":"Down Woody Material Microplot Fuel Table","description":"Down woody material microplot fuel table. This table provides information on the fuel loads (shrubs and herbs) measured on the microplot. <br><br>* PLOT.CN = DWM_MICROPLOT_FUEL.PLT_CN links the microplot fuel records to the unique plot record.","msft:item_name":"dwm_microplot_fuel"},{"name":"Down Woody Material Residual Pile Table","description":"Down woody material residual pile table. This table provides information on the wood piles measured on the subplot. <br><br>* PLOT.CN = DWM_RESIDUAL_PILE.PLT_CN links the wood piles records to the unique plot record. <br><br>* COND.PLT_CN = DWM_RESIDUAL_PILE.PLT_CN and COND.CONDID= DWM_RESIDUAL_PILE.CONDID links the wood piles records to the unique condition record.","msft:item_name":"dwm_residual_pile"},{"name":"Down Woody Material Transect Segment Table","description":"Down woody material transect segment table. This table describes the down woody material transect segment lengths by condition class. <br><br>* PLOT.CN = DWM_TRANSECT_SEGMENT.PLT_CN links the down woody material transect length records to the unique plot record. <br><br>* COND.PLT_CN = DWM_TRANSECT_SEGMENT.PLT_CN and COND.CONDID= DWM_TRANSECT_SEGMENT.CONDID links the down woody material transect segment records to the unique condition record.","msft:item_name":"dwm_transect_segment"},{"name":"Condition Down Woody Material Calculation Table","description":"Condition down woody material calculation table. This table contains calculated values and condition-level estimates for down woody attributes by plot number (PLOT), condition class number (CONDID), and evaluation identifier (EVALID). <br><br>* PLOT.CN = COND_DWM_CALC.PLT_CN links the down woody material calculation records to the unique plot record. <br><br>* COND.CN = COND_DWM_CALC.CND_CN links the down woody material calculation records to the unique condition record. <br><br>* POP_STRATUM. CN = COND_DWM_CALC.STRATUM_CN links the down woody material calculation records to the unique population stratum record.","msft:item_name":"cond_dwm_calc"},{"name":"Plot Regeneration Table","description":"Plot regeneration table. This table contains the information for the four subplots describing the amount of animal browse pressure exerted on the regeneration of trees. <br><br>* PLOT.CN = PLOT_REGEN.PLT_CN links the unique plot record to the unique plot regeneration record.","msft:item_name":"plot_regen"},{"name":"Subplot Regeneration Table","description":"Subplot regeneration table. This table provides information on the subplot survey status and the site survey limitations, if any, for the tree regeneration study. <br><br>* PLOT.CN = SUBPLOT_REGEN.PLT_CN links the unique plot record to the subplot regeneration records. <br><br>* SUBPLOT.PLT_CN = SUBPLOT_REGEN.PLT_CN and SUBPLOT.SUBP = SUBPLOT_REGEN.SUBP links the subplot record to the subplot regeneration record.","msft:item_name":"subplot_regen"},{"name":"Seedling Regeneration Table","description":"Seedling regeneration. This table contains provides information on the seedling count by condition, species, source, and length class for the tree regeneration study. <br><br>* PLOT.CN = SEEDLING_REGEN.PLT_CN links the unique plot record to the seedling regeneration records. <br><br>* COND.PLT_CN = SEEDLING_REGEN.PLT_CN and COND.CONDID = SEEDLING_REGEN.CONDID links the regeneration seedling records to the unique condition record.","msft:item_name":"seedling_regen"},{"name":"Population Estimation Unit Table","description":"Population estimation unit table. This table contains information about estimation units. An estimation unit is a geographic area that can be drawn on a map. It has a known area, and the sampling intensity must be the same within a stratum within an estimation unit. Generally, estimation units are contiguous areas, but exceptions are made when certain ownerships, usually National Forests, are sampled at different intensities. One record in the POP_ESTN_UNIT table corresponds to a single estimation unit. POP_ESTN_UNIT.CN = POP_STRATUM.ESTN_UNIT_CN links the unique stratified geographical area (ESTN_UNIT) to the strata (STRATUMCD) that are assigned to each ESTN_UNIT.","msft:item_name":"pop_estn_unit"},{"name":"Population Evaluation Table","description":"Population evaluation table. This table provides information about evaluations. An evaluation is the combination of a set of plots (the sample) and a set of Phase 1 data (obtained through remote sensing, called a stratification) that can be used to produce population estimates for a State (an evaluation may be created to produce population estimates for a region other than a State, such as the Black Hills National Forest). A record in the POP_EVAL table identifies one evaluation and provides some descriptive information about how the evaluation may be used. <br><br>* POP_ESTN_UNIT.EVAL_CN = POP_EVAL.CN links the unique evaluation identifier (EVALID) in the POP_EVAL table to the unique geographical areas (ESTN_UNIT) that are stratified. Within a population evaluation (EVALID) there can be multiple population estimation units, or geographic areas across which there are a number of values being estimated (e.g., estimation of volume across counties for a given State).","msft:item_name":"pop_eval"},{"name":"Population Evaluation Attribute Table","description":"Population evaluation attribute table. This table provides information as to which population estimates can be provided by an evaluation. 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contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent.  These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"table:storage_options":{"account_name":"soils"}}},"stac_version":"1.0.0","table:tables":[{"name":"chaashto","description":"The Horizon AASHTO table contains the American Association of State Highway Transportation Officials classification(s) for the referenced horizon.  One row in this table is marked as the representative AASHTO classification for the horizon."},{"name":"chconsistence","description":"The Horizon Consistence table contains descriptive terms of soil consistence -- rupture resistance, plasticity, and stickiness -- for the referenced horizon.  One row in this table is marked as having the representative characteristics for the horizon."},{"name":"chdesgnsuffix","description":"The Horizon Designation Suffix table contains the designation suffix(es), one per row,  for the referenced horizon.  For example, the \"h\" and \"s\" of a Bhs horizon appear as two rows in this table."},{"name":"chfrags","description":"The Horizon Fragments table lists the mineral and organic fragments that generally occur in the referenced horizon.  If the Volume % is greater than zero (low=5, RV=10, high=15) in a row, the kind and size of fragment in that row exists everywhere this horizon and component occur in the map unit.  If the Volume % includes zero (low=0, RV=5, high=10), the kind and size of fragment may exist in some places, but not in others."},{"name":"chorizon","description":"The Horizon table lists the horizon(s) and related data for the referenced map unit component.  If the horizon thickness is greater than zero (low=5, RV=8, high=12), the horizon exists everywhere this component occurs.  If the horizon thickness includes zero (low=0, RV=1, high=3), the horizon may exist in some places, but not in other places.\r\nHorizons that have two distinct parts, such as E/B or E&Bt horizons, are recorded twice.  Once for the characteristics of the first part; and again on another row, using the same depths and thicknesses, for the characteristics of the other part."},{"name":"chpores","description":"The Horizon Pores table lists the voids for the referenced horizon.  If the Quantity is greater than zero (low=2, RV=5, high=10) in a row, the voids in that row exist everywhere the horizon and component occur in the map unit.  If the Quantity includes zero (low=0, RV=2, high=5), the voids may exist in some places, but not in others.  More than one row can be marked as an RV row because a horizon may have more than one size or shape of void."},{"name":"chstruct","description":"The Horizon Structure table lists the individual soil structure size, grade, and shape terms for the referenced horizon.  Terms in this table are assembled into a structure group string which is recorded in the Horizon Structure Group table."},{"name":"chstructgrp","description":"The Horizon Structure Group table lists the ranges of  soil structure for the referenced horizon.  The row with the typically occurring structure is marked as being representative.  The entry in this table is based on grouping of entries in the Horizon Structure table."},{"name":"chtext","description":"The Horizon Text table contains notes and narrative descriptions related to the referenced horizon.  Some notes may provide additional information about the horizon for which there is no explicit column for such data.  In many cases, the table is empty for a particular horizon."},{"name":"chtexture","description":"The Horizon Texture table lists the individual texture(s), or term(s) used in lieu of texture, for the referenced horizon.  Only the unmodified texture terms are listed in the Horizon Texture table; modifiers are listed in the Horizon Texture Modifier table.  For example, a gravelly loamy sand is shown as \"GR-LS\" in the Horizon Texture Group table, \"ls\" in the Horizon Texture table, and \"gr\" in the Horizon Texture Modifier table."},{"name":"chtexturegrp","description":"The Horizon Texture Group table lists the range of textures for the referenced horizon as a concatenation of horizon texture and texture modifier(s).  For example, a horizon that is gravelly loamy sand in some places and gravelly loamy coarse sand in other places is shown as GR-LS on one row and GR-LCOS on another row in this table.  The row with the typically occurring texture is identified as the RV row.  Stratified textures are shown in one row.  For example, a horizon that is stratified gravelly loamy fine sand and cobbly coarse sand is shown as SR- GR-LFS CB-COS on one row and the Stratified? column for that row is marked \"yes\".  If two or more textures always occur together but are not stratified, all of the textures are listed on one row and the Stratified? column for that row is marked \"no\"."},{"name":"chtexturemod","description":"The Horizon Texture Modifier table lists the texture modifier(s) for the referenced texture.  For example, a gravelly loamy sand is shown as \"GR-LS\" in the Horizon Texture Group table, \"ls\" in the Horizon Texture table, and \"gr\" in this table."},{"name":"chunified","description":"The Horizon Unified table contains the Unified Soil Classification(s) for the referenced horizon.  One row in the Horizon Unified table is marked as the representative Unified classification for the horizon."},{"name":"cocanopycover","description":"The Component Canopy Cover table lists the overstory plants that typically occur on the referenced map unit component."},{"name":"cocropyld","description":"The Component Crop Yield table lists commonly grown crops and their expected range in yields when grown on the referenced map unit component.  Yields for the map unit as a whole are given in the Mapunit Crop Yield table."},{"name":"codiagfeatures","description":"The Component Diagnostic Features table lists the typical soil features, such as ochric epipedon or cambic horizon, for the referenced map unit component."},{"name":"coecoclass","description":"The Component Ecological Classification table identifies the ecological sites typically associated with the referenced map unit component.  These may include the official NRCS forestland and rangland ecological sites, as well as those of other classification systems, such as the USFS Habitat Types."},{"name":"coeplants","description":"The Component Existing Plants table lists the plants, either rangeland or forestland plants, that typically occur on the referenced map unit component."},{"name":"coerosionacc","description":"The Component Erosion Accelerated table lists the kinds of accelerated erosion that occur on the referenced map unit component.  One row in this table is marked as the representative kind of accelerated erosion for that component."},{"name":"coforprod","description":"The Component Forest Productivity table lists the site index and the annual productivity in cubic feet per acre per year (CAMI) of forest overstory tree species that typically occur on the referenced map unit component."},{"name":"coforprodo","description":"The Component Forest Productivity - Other table lists the site index and annual productivity of forest overstory tree species in units other than cubic feet per acre per year for trees that typically occur on the referenced map unit component."},{"name":"cogeomordesc","description":"The Component Geomorphic Description table lists the geomorphic features on which the referenced map unit component typically occurs."},{"name":"cohydriccriteria","description":"The Component Hydric Criteria table lists the hydric soil criteria met for those referenced map unit components that are classified as a \"hydric soil.\""},{"name":"cointerp","description":"The Component Interpretation table lists the predictions of behavior and limiting features for specified uses made for the referenced map unit component."},{"name":"comonth","description":"The Component Month table lists the monthly flooding and ponding characteristics for the referenced map unit component.  This table has one row for each month of the year."},{"name":"component","description":"The Component table lists the map unit components identified in the referenced map unit, and selected properties of each component.  If the Component % is greater than zero (low=65, RV=75, high=90) for a component, that component exists in every delineation of that mapunit.  If the Component % includes zero (low=0, RV=50, high=90), the component may exist in some delineations, but not in others."},{"name":"copm","description":"The Component Parent Material table lists the individual parent material(s) for the referenced map unit component.    In some cases where soils developed in multiple materials in a vertical sequence, that sequence will be noted.  In other cases multiple entries with no vertical sequence noted indicates the soil may have formed in one of the materials listed."},{"name":"copmgrp","description":"The Component Parent Material Group table lists the concatenated string of parent material(s) in which the referenced map unit component formed based on entries in the Component Parent Material table.  For example, a component formed in one parent material, such as loess, or one vertical sequence of parent materials, such as loamy glacial drift over silty residuum weathered from shale, has one row in this table.  A component formed in one parent material in some locations, but another parent material (or sequence of parent materials) in other locations has two rows in this table, one for each parent material (or sequence of parent materials).  One row is identified as the representative parent material."},{"name":"copwindbreak","description":"The Component Potential Windbreak table lists the windbreak plant species commonly recommended for the referenced map unit component.  A windbreak plant listed in this table may be used alone or in combination with other plants."},{"name":"corestrictions","description":"The Component Restrictions table lists the root restrictive feature(s) or layer(s) for the referenced map unit component.  If the thickness of the restrictive layer is greater than zero (low=5, RV=8, high=10), the restrictive layer exists in all delineations of the map unit where the component occurs.  If the thickness of the restrictive layer includes zero (low=0, RV=2, high=5), the restrictive layer may exist in some delineations, but not in others.  This table will be empty if the component does not have restrictive features, but could have several rows if several restrictive features occur in the soil."},{"name":"cosoilmoist","description":"The Component Soil Moisture table describes the typical soil moisture profile for the referenced map unit component during the month referenced in the Component Month table.  The soil moisture profiles for each month, taken as a group of twelve months, describe the representative situation for the component throughout the year."},{"name":"cosoiltemp","description":"The Component Soil Temperature table describes the typical soil temperature profile for the referenced map unit component during the month referenced in the Component Month table.  The soil temperature profiles for each month, taken as a group of twelve months, describe the representative situation for the component throughout the year."},{"name":"cosurffrags","description":"The Component Surface Fragments table lists the organic or mineral fragments that generally occur on the surface of the referenced map unit component.  If the cover percent is greater than zero (low=0.1, RV=1, high=3) for a row in this table, the fragment is in every delineation of the map unit where the referenced component occurs.  If the Cover % includes zero (low=0, RV=0.01, high=1) for a row in this table, the fragment may exist in some delineations and not in others."},{"name":"cosurfmorphgc","description":"The Component Three Dimensional Surface Morphometry table lists the typical geomorphic position (s) of the referenced map unit component, in three dimension terms.  The geomorphic position(s) listed in this table apply to the geomorphic feature referenced in the Component Geomorphic Description table."},{"name":"cosurfmorphhpp","description":"The Component Two Dimensional Surface Morphometry table lists the geomorphic position(s) of the referenced map unit component, in two dimensional hillslope profile terms.  The geomorphic position(s) listed in this table apply to the geomorphic feature referenced  in the Component Geomorphic Description table."},{"name":"cosurfmorphmr","description":"The Component Microrelief Surface Morphometry table lists microrelief features associated with the referenced geomorphic (microfeature) feature shown in the Component Geomorphic Description table."},{"name":"cosurfmorphss","description":"The Component Slope Shape Surface Morphometry table lists the geomorphic shape(s) of the referenced map unit component, in slope shape terms.  The slope shape terms listed in this table apply to the referenced geomorphic feature shown in the Component Geomorphic Description table."},{"name":"cotaxfmmin","description":"The Component Taxonomic Family Mineralogy table lists the mineralogy characteristics, as defined in Soil Taxonomy, that apply to the referenced map unit component."},{"name":"cotaxmoistcl","description":"The Component Taxonomic Moisture Class table provides clear identification of the intended taxonomic moisture class, as defined in Soil Taxonomy, that apply to the referenced map unit component, even though moisture class is implied at a higher taxonomic level.  The class or classes listed in this table describe the representative situation for the component."},{"name":"cotext","description":"The Component Text table contains notes and narrative descriptions for the referenced map unit component.  In many cases, the table will be empty for a particular component."},{"name":"cotreestomng","description":"The Component Trees To Manage table lists the trees commonly recommended for managing on the referenced map unit component."},{"name":"cotxfmother","description":"The Component Taxonomic Family Other Criteria table lists the other taxonomic characteristics, such as classes of coatings or permanent cracks, as defined in Soil Taxonomy, that apply to the referenced map unit component.  The characteristics listed in this table describe the representative situation for the component."},{"name":"distinterpmd","description":"The Distribution Interp Metadata table records the set of NASIS fuzzy logic interpretations which were generated for the map unit components included in a set of distribution data."},{"name":"distlegendmd","description":"The Distribution Legend Metadata table records information about the legends or soil survey areas selected for inclusion in a set of distributed data.  The presence of a legend in this table does not imply that all of the available data for that legend was included in the set of data that was distributed.  Only certain map units and components for that legend may have been selected.  The record of the criteria used for selecting map units and components may be found in the Distribution Metadata table."},{"name":"distmd","description":"The Distribution Metadata table records information associated with the selection of a set of data for distribution to some entity or information system external to NASIS.  A set of distribution data may include only selected map units from a legend or legends, and only selected components of those map units.  This table records the criteria used for selecting map units and components for inclusion in the set of distributed data.  Other recorded information includes the name of the NASIS user who initiated a distribution request, and the times when that request was made, and when that request was ultimately processed."},{"name":"laoverlap","description":"The Legend Area Overlap table lists the geographic areas that are coincident with the soil survey area identified in the Legend table.  For example, a survey area that covers two counties would have two rows in this table, one for each county.  Other types of geographic areas listed might include state, MLRA, rainfall (R) factor area, climate (C) factor area, etc."},{"name":"legend","description":"The Legend table identifies the soil survey area that the legend is related to, and related information about that legend."},{"name":"legendtext","description":"The Legend Text table contains notes and narrative descriptions related to the referenced legend.  Legend text is optional.  In many cases, this table is empty."},{"name":"mapunit","description":"The Mapunit table identifies the map units included in the referenced legend.  Data related the map unit as a whole are also given."},{"name":"muaggatt","description":"The Mapunit Aggregated Attribute table records a variety of soil attributes and interpretations that have been aggregated from the component level to a single value at the map unit level. They have been aggregated by one or more appropriate means in order to express a consolidated value or interpretation for the map unit as a whole."},{"name":"muaoverlap","description":"The Mapunit Area Overlap table lists the map units that exist in the overlap between the entire soil survey area and the referenced geographic area in the Legend Area Overlap table."},{"name":"mucropyld","description":"The Mapunit Crop Yield table lists commonly grown crops and their expected yields for the referenced map unit as a whole.  Yields for individual map unit components are given in the Component Crop Yield table."},{"name":"mutext","description":"The Mapunit Text table contains notes and narrative descriptions related to the referenced map unit."},{"name":"sacatalog","description":"This table records the primary dynamic metadata associated with a soil survey area.  This includes such things as survey area version, tabular data version, etc. The remaining dynamic metadata, which soil interpretations were generated for the corresponding soil survey area, is recorded in the Survey Area Interpretation table."},{"name":"sainterp","description":"This table records information about the soil interpretations that were generated for a soil survey area."},{"name":"valu1","description":"Included with the gSSURGO database, but not a part of the standard SSURGO dataset is a table called Valu1. This table contains 57 pre-summarized or 'ready to map' attributes derived from the official SSURGO database. These attribute data are pre-summarized to the map unit level using best-practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and gridded SSURGO (gSSURGO) datasets."}],"msft:group_id":"gnatsgo","msft:container":"gnatsgo-stac","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"soils","msft:short_description":"The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories.","msft:region":"westeurope"},{"id":"hgb","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb"},{"rel":"license","href":"https://earthdata.nasa.gov/earth-observation-data/data-use-policy","title":"EOSDIS Data Use Policy"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/hgb","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"HGB: Harmonized Global Biomass for 2010","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hgb.png","type":"image/png","roles":["thumbnail"],"title":"Harmonized Global Biomass"},"geoparquet-items":{"href":"abfs://items/hgb.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-61.002778,180.0,84.0]]},"temporal":{"interval":[["2010-12-31T00:00:00Z","2010-12-31T00:00:00Z"]]}},"license":"proprietary","keywords":["Biomass","Carbon","ORNL"],"providers":[{"url":"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763","name":"Oak Ridge National Laboratory Distributed Active Archive Center","roles":["producer","licensor"]},{"url":"https://carbonplan.org","name":"CarbonPlan","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[300]},"description":"This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n","item_assets":{"aboveground":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Global above-ground biomass"},"belowground":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Global below-ground biomass"},"aboveground_uncertainty":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Uncertainty associated with global above-ground biomass"},"belowground_uncertainty":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Uncertainty associated with global below-ground biomass"}},"stac_version":"1.0.0","msft:container":"cpdata","stac_extensions":["https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"cpdataeuwest","msft:short_description":"Global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution","msft:region":"westeurope"},{"id":"cop-dem-glo-30","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30"},{"rel":"license","href":"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf","title":"Copernicus DEM License"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-30","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Copernicus DEM GLO-30","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem.png","type":"image/png","roles":["thumbnail"],"title":"COP DEM GLO-30"},"geoparquet-items":{"href":"abfs://items/cop-dem-glo-30.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-04-22T00:00:00Z","2021-04-22T00:00:00Z"]]}},"license":"proprietary","keywords":["Copernicus","DEM","DSM","Elevation"],"providers":[{"url":"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf","name":"European Space Agency","roles":["licensor"]},{"url":"https://registry.opendata.aws/copernicus-dem/","name":"Sinergise","roles":["producer","processor"]},{"url":"https://doi.org/10.5069/G9028PQB","name":"OpenTopography","roles":["host"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"gsd":[30],"platform":["tandem-x"]},"description":"The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"]}},"stac_version":"1.0.0","msft:group_id":"copernicus-dem","msft:container":"copernicus-dem-stac","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"elevationeuwest","msft:short_description":"Copernicus DEM 30 is a near-global digital surface model (DSM) with a horizontal resolution of approximately 30 meters, derived from radar satellite data acquired during the TanDEM-X mission.","msft:region":"westeurope"},{"id":"cop-dem-glo-90","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90"},{"rel":"license","href":"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf","title":"Copernicus DEM License"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Copernicus DEM GLO-90","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem-glo-90.png","type":"image/png","roles":["thumbnail"],"title":"COP DEM GLO-90"},"geoparquet-items":{"href":"abfs://items/cop-dem-glo-90.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-04-22T00:00:00Z","2021-04-22T00:00:00Z"]]}},"license":"proprietary","keywords":["Copernicus","DEM","Elevation"],"providers":[{"url":"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf","name":"European Space Agency","roles":["licensor"]},{"url":"https://registry.opendata.aws/copernicus-dem/","name":"Sinergise","roles":["producer","processor"]},{"url":"https://doi.org/10.5069/G9028PQB","name":"OpenTopography","roles":["host"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"gsd":[90],"platform":["tandem-x"]},"description":"The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: <https://doi.org/10.5069/G9028PQB>\n\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"]}},"stac_version":"1.0.0","msft:group_id":"copernicus-dem","msft:container":"copernicus-dem-stac","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"elevationeuwest","msft:short_description":"Copernicus DEM 90 is a near-global digital surface model (DSM) with a horizontal resolution of approximately 90 meters, derived from radar satellite data acquired during the TanDEM-X mission.","msft:region":"westeurope"},{"id":"terraclimate","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate"},{"rel":"license","href":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","type":"text/html","title":"CC0-1.0 License"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/terraclimate","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"TerraClimate","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/terraclimate.png","role":["thumbnail"],"type":"image/png","title":"TerraClimate thumbnail"},"zarr-abfs":{"href":"az://cpdata/terraclimate.zarr","type":"application/vnd+zarr","roles":["data","zarr","abfs"],"title":"TerraClimate Azure Blob File System Zarr root","description":"Azure Blob File System URI of the TerraClimate Zarr Group on Azure Blob Storage for use with adlfs.","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"cpdataeuwest"}}},"zarr-https":{"href":"https://cpdataeuwest.blob.core.windows.net/cpdata/terraclimate.zarr","type":"application/vnd+zarr","roles":["data","zarr","https"],"title":"TerraClimate HTTPS Zarr root","description":"HTTPS URI of the TerraClimate Zarr Group on Azure Blob Storage.","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1958-01-01T00:00:00Z","2021-12-01T00:00:00Z"]]}},"license":"CC0-1.0","keywords":["TerraClimate","Water","Precipitation","Temperature","Vapor Pressure","Climate"],"providers":[{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]},{"url":"http://www.climatologylab.org/terraclimate.html","name":"Climatology Lab","roles":["producer"]},{"url":"https://www.nature.com/articles/sdata2017191","name":"Abatzoglou, J.T., S.Z. 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This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n","stac_version":"1.0.0","cube:variables":{"q":{"type":"data","unit":"mm","attrs":{"units":"mm","long_name":"runoff_amount","dimensions":"lon lat time","description":"Runoff","grid_mapping":"crs","standard_name":"runoff_amount","coordinate_system":"WGS84,EPSG:4326"},"shape":[768,4320,8640],"chunks":[12,1024,1024],"dimensions":["time","lat","lon"],"description":"Runoff"},"ws":{"type":"data","unit":"m/s","attrs":{"units":"m/s","long_name":"wind_speed","dimensions":"lon lat time","description":"Wind Speed at 10-m","grid_mapping":"crs","standard_name":"wind_speed","coordinate_system":"WGS84,EPSG:4326"},"shape":[768,4320,8640],"chunks":[12,1024,1024],"dimensions":["time","lat","lon"],"description":"Wind Speed at 10-m"},"aet":{"type":"data","unit":"mm","attrs":{"units":"mm","long_name":"water_evaporation_amount","dimensions":"lon lat time","description":"Actual 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four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- **model** is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- **scenario** is one of \"historical\", \"ssp245\" or \"ssp585\".\n- **variable** is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- **year** depends on the value of *scenario*. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some *experimental* **reference files** as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.","item_assets":{"pr":{"type":"application/netcdf","roles":["data"],"title":"Precipitation","description":"Precipitation"},"tas":{"type":"application/netcdf","roles":["data"],"title":"Daily Near-Surface Air Temperature","description":"Daily Near-Surface Air Temperature"},"hurs":{"type":"application/netcdf","roles":["data"],"title":"Near-Surface Relative Humidity","description":"Near-Surface Relative Humidity"},"huss":{"type":"application/netcdf","roles":["data"],"title":"Near-Surface Specific Humidity","description":"Near-Surface Specific Humidity"},"rlds":{"type":"application/netcdf","roles":["data"],"title":"Surface Downwelling Longwave Radiation","description":"Surface Downwelling Longwave Radiation"},"rsds":{"type":"application/netcdf","roles":["data"],"title":"Surface Downwelling Shortwave Radiation","description":"Surface Downwelling Shortwave Radiation"},"tasmax":{"type":"application/netcdf","roles":["data"],"title":"Daily Maximum Near-Surface Air Temperature","description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"application/netcdf","roles":["data"],"title":"Daily Minimum Near-Surface Air Temperature","description":"Daily Minimum Near-Surface Air Temperature"},"sfcWind":{"type":"application/netcdf","roles":["data"],"title":"Daily-Mean Near-Surface Wind Speed","description":"Daily-Mean Near-Surface Wind Speed"}},"sci:citation":"Climate scenarios used were from the NEX-GDDP-CMIP6 dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).","stac_version":"1.0.0","cube:variables":{"pr":{"type":"data","unit":"kg m-2 s-1","attrs":{"units":"kg m-2 s-1","comment":"includes both liquid and solid phases","long_name":"Precipitation","cell_methods":"area: time: mean","cell_measures":"area: areacella","standard_name":"precipitation_flux"},"shape":[365,600,1440],"dimensions":["time","lat","lon"],"description":"Precipitation"},"tas":{"type":"data","unit":"K","attrs":{"units":"K","comment":"near-surface (usually, 2 meter) air temperature; derived from downscaled tasmax & tasmin","long_name":"Daily Near-Surface Air Temperature","cell_methods":"area: mean time: maximum","cell_measures":"area: areacella","standard_name":"air_temperature"},"shape":[365,600,1440],"dimensions":["time","lat","lon"],"description":"Daily Near-Surface Air Temperature"},"hurs":{"type":"data","unit":"%","attrs":{"units":"%","comment":"The relative humidity with respect to liquid water for T> 0 C, and with respect to ice for T<0 C.","long_name":"Near-Surface Relative Humidity","cell_methods":"area: time: mean","cell_measures":"area: areacella","standard_name":"relative_humidity"},"shape":[365,600,1440],"dimensions":["time","lat","lon"],"description":"Near-Surface Relative Humidity"},"huss":{"type":"data","unit":"1","attrs":{"units":"1","comment":"Near-surface (usually, 2 meter) specific humidity.","long_name":"Near-Surface Specific Humidity","cell_methods":"area: time: mean","cell_measures":"area: areacella","standard_name":"specific_humidity"},"shape":[365,600,1440],"dimensions":["time","lat","lon"],"description":"Near-Surface Specific Humidity"},"rlds":{"type":"data","unit":"W m-2","attrs":{"units":"W m-2","comment":"The surface called 'surface' means the lower boundary of the atmosphere. 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Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gpm-imerg-hhr/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/gpm-imerg-hhr","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"GPM IMERG","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gpm-imerg-hhr.png","role":["thumbnail"],"type":"image/png","title":"gpm-imerg-hhr thumbnail"},"zarr-abfs":{"href":"abfs://imerg/gpm-imerg-hhr.zarr","type":"application/vnd+zarr","roles":["data","zarr"],"description":"Azure Blob File System URI of the gpm-imerg-hhr Zarr Group on Azure Blob Storage for use with adlfs.","xarray:open_kwargs":{"use_cftime":true,"consolidated":true},"xarray:storage_options":{"account_name":"ai4edataeuwest"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-01T00:00:00Z","2021-05-31T23:30:00Z"]]}},"license":"proprietary","sci:doi":"10.5067/GPM/IMERG/3B-HH/06","keywords":["IMERG","GPM","Precipitation"],"providers":[{"url":"https://developmentseed.org/","name":"Development Seed","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]},{"url":"https://gpm.nasa.gov/data/directory","name":"NASA","roles":["producer"]}],"description":"The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.","sci:citation":"Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC)","stac_version":"1.0.0","cube:variables":{"lat_bnds":{"type":"auxiliary","unit":"degrees_north","attrs":{"Units":"degrees_north","units":"degrees_north","coordinates":"lat latv","DimensionNames":"lat,latv"},"shape":[1800,2],"chunks":[1800,2],"dimensions":["lat","latv"]},"lon_bnds":{"type":"auxiliary","unit":"degrees_east","attrs":{"Units":"degrees_east","units":"degrees_east","coordinates":"lon lonv","DimensionNames":"lon,lonv"},"shape":[3600,2],"chunks":[3600,2],"dimensions":["lon","lonv"]},"time_bnds":{"type":"auxiliary","attrs":{"Units":"seconds since 1970-01-01 00:00:00 UTC","coordinates":"time 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Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation over the majority of the Earth's surface.","msft:region":"westeurope"},{"id":"gnatsgo-rasters","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters"},{"rel":"about","href":"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf","type":"application/pdf","title":"gSSURGO User Guide","description":"User guide for gSSURGO"},{"rel":"about","href":"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1464658&ext=pdf","type":"application/pdf","title":"gNATSGO Overview Slides","description":"Slides giving a high level overview of the gNATSGO dataset"},{"rel":"license","href":"https://creativecommons.org/publicdomain/zero/1.0/","title":"CC0 1.0 Universal Public Domain Dedication"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"gNATSGO Soil Database - Rasters","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-rasters.png","type":"image/png","roles":["thumbnail"],"title":"gNATSGO"},"geoparquet-items":{"href":"abfs://items/gnatsgo-rasters.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet 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contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent.  These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)","item_assets":{"mukey":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"mukey","description":"Map unit key is the unique identifier of a record in the Mapunit table. Use this column to join the Component table to the Map Unit table and the Valu1 table to the MapUnitRaster_10m raster map layer to map valu1 themes."},"aws0_5":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_5","description":"Available water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"soc0_5":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_5","description":"Soil organic carbon stock estimate (SOC) in standard layer 1 or standard zone 1 (0-5 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 5 cm. NULL values are presented where data are incomplete or not available."},"tk0_5a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_5a","description":"Thickness of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_5s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_5s","description":"Thickness of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"aws0_20":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_20","description":"Available water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws0_30":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_30","description":"Available water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws5_20":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws5_20","description":"Available water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"soc0_20":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_20","description":"Soil organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 20 cm. NULL values are presented where data are incomplete or not available."},"soc0_30":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_30","description":"Soil organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 30 cm. NULL values are presented where data are incomplete or not available."},"soc5_20":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc5_20","description":"Soil organic carbon stock estimate (SOC) in standard layer 2 (5-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 5-20 cm layer. NULL values are presented where data are incomplete or not available."},"tk0_20a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_20a","description":"Thickness of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_20s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_20s","description":"Thickness of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk0_30a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_30a","description":"Thickness of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_30s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_30s","description":"Thickness of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk5_20a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk5_20a","description":"Thickness of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk5_20s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk5_20s","description":"Thickness of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"aws0_100":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_100","description":"Available water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws0_150":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_150","description":"Available water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws0_999":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws0_999","description":"Available water storage estimate (AWS) in total soil profile (0 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws20_50":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws20_50","description":"Available water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"droughty":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"droughty","description":"zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as \"1\" for a drought vulnerable soil landscape map unit or \"0\" for a non-droughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies) or where data were not available. It is computed as a weighted average for major earthy components. Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes'"},"nccpi3sg":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"nccpi3sg","description":"National Commodity Crop Productivity Index for Small Grains (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"soc0_100":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_100","description":"Soil organic carbon stock estimate (SOC) in standard zone 4 (0-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 100 cm. NULL values are presented where data are incomplete or not available."},"soc0_150":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_150","description":"Soil organic carbon stock estimate (SOC) in standard zone 5 (0-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 150 cm. NULL values are presented where data are incomplete or not available."},"soc0_999":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc0_999","description":"Soil organic carbon stock estimate (SOC) in total soil profile (0 cm to the reported depth of the soil profile). The concentration of organic carbon present in the soil expressed in grams C per square meter for the total reported soil profile depth. NULL values are presented where data are incomplete or not available."},"soc20_50":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc20_50","description":"Soil organic carbon stock estimate (SOC) in standard layer 3 (20-50 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 20-50 cm layer. NULL values are presented where data are incomplete or not available."},"tk0_100a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_100a","description":"Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_100s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_100s","description":"Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk0_150a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_150a","description":"Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_150s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_150s","description":"Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk0_999a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_999a","description":"Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk0_999s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk0_999s","description":"Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk20_50a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk20_50a","description":"Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk20_50s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk20_50s","description":"Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"aws50_100":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws50_100","description":"Available water storage estimate (AWS) in standard layer 3 (50-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"musumcpct":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"musumcpct","description":"The sum of the comppct_r (SSURGO component table) values for all listed components in the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available."},"nccpi3all":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"nccpi3all","description":"National Commodity Crop Productivity Index that has the highest value among Corn and Soybeans, Small Grains, or Cotton (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"nccpi3cot":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"nccpi3cot","description":"National Commodity Crop Productivity Index for Cotton (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"nccpi3soy":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"nccpi3soy","description":"National Commodity Crop Productivity Index for Soybeans (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"pwsl1pomu":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"pwsl1pomu","description":"Potential Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit that meets the PWSL criteria. The hydric rating (soil component variable “hydricrating”) is an indicator of wet soils. For version 1 (pwsl1), those soil components that meet the following criteria are tagged as PWSL and their comppct_r values are summed for each map unit. Soil components with hydricrating = 'YES' are considered PWSL. Soil components with hydricrating = “NO” are not PWSL. Soil components with hydricrating = 'UNRANKED' are tested using other attributes, and will be considered PWSL if any of the following conditions are met: drainagecl = 'Poorly drained' or 'Very poorly drained' or the localphase or the otherph data fields contain any of the phrases \"drained\" or \"undrained\" or \"channeled\" or \"protected\" or \"ponded\" or \"flooded\". If these criteria do not determine the PWSL for a component and hydricrating = 'UNRANKED', then the map unit will be classified as PWSL if the map unit name contains any of the phrases \"drained\" or \"undrained\" or \"channeled\" or \"protected\" or \"ponded\" or \"flooded\". For version 1 (pwsl1), waterbodies are identified as \"999\" when map unit names match a list of terms that identify water or intermittent water or map units have a sum of the comppct_r for \"Water\" that is 80% or greater. NULL values are presented where data are incomplete or not available."},"rootznaws":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"rootznaws","description":"Root zone (commodity crop) available water storage estimate (RZAWS) , expressed in mm, is the volume of plant available water that the soil can store within the root zone based on all map unit earthy major components (weighted average). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"rootznemc":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"rootznemc","description":"Root zone depth is the depth within the soil profile that commodity crop (cc) roots can effectively extract water and nutrients for growth. Root zone depth influences soil productivity significantly. Soil component horizon criteria for root-limiting depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, a dense layer, a layer having a pH of less than 3.5, or a layer having an electrical conductivity of more than 12 within the component soil profile. If no root-restricting zone is identified, a depth of 150 cm is used to approximate the root zone depth (Dobos et al., 2012). Root zone depth is computed for all map unit major earthy components (weighted average). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"soc50_100":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc50_100","description":"Soil organic carbon stock estimate (SOC) in standard layer 4 (50-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 50-100 cm layer. NULL values are presented where data are incomplete or not available."},"tk50_100a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk50_100a","description":"Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk50_100s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk50_100s","description":"Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"aws100_150":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws100_150","description":"Available water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"aws150_999":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"aws150_999","description":"Available water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available."},"musumcpcta":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"musumcpcta","description":"The sum of the comppct_r (SSURGO component table) values used in the available water storage calculation for the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available."},"musumcpcts":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"musumcpcts","description":"The sum of the comppct_r (SSURGO component table) values used in the soil organic carbon calculation for the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available."},"nccpi3corn":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"nccpi3corn","description":"National Commodity Crop Productivity Index for Corn (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available."},"pctearthmc":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"pctearthmc","description":"The National Commodity Crop Productivity Index map unit percent earthy is the map unit summed comppct_r for major earthy components. Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). Useful metadata information. NULL values are presented where data are incomplete or not available."},"soc100_150":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc100_150","description":"Soil organic carbon stock estimate (SOC) in standard layer 5 (100-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 100-150 cm layer. NULL values are presented where data are incomplete or not available."},"soc150_999":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"soc150_999","description":"Soil organic carbon stock estimate (SOC) in standard layer 6 (150 cm to the reported depth of the soil profile). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 150 cm and greater depth layer. NULL values are presented where data are incomplete or not available."},"tk100_150a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk100_150a","description":"Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk100_150s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk100_150s","description":"Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."},"tk150_999a":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk150_999a","description":"Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available."},"tk150_999s":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"tk150_999s","description":"Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available."}},"stac_version":"1.0.0","msft:group_id":"gnatsgo","msft:container":"gnatsgo-stac","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"soils","msft:short_description":"Raster data for the gridded National Soil Survey Geographic Database (gNATSGO), a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories.","msft:region":"westeurope"},{"id":"3dep-lidar-hag","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-hag","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Height above Ground","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hag-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"},"geoparquet-items":{"href":"abfs://items/3dep-lidar-hag.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","Elevation"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[2.0]},"description":"This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"COG data","raster:bands":[{"name":"HeightAboveGround","unit":"metre","sampling":"point","data_type":"float32","description":"Raster for Height Above Ground (HAG)"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json#","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for the Height Above Ground (HAG or Elevation) Cloud Optimized Geotiffs (COGs). All USGS HAG COG STAC items will be associated with this STAC collection.","msft:region":"westeurope"},{"id":"io-lulc-annual-v02","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02"},{"rel":"related","href":"https://livingatlas.arcgis.com/landcover/"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"CC BY 4.0"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"10m Annual Land Use Land Cover (9-class) V2","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-annual-v02.png","title":"10m Annual Land Use Land Cover (9-class)","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/io-lulc-annual-v02.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2024-01-01T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Global","Land Cover","Land Use","Sentinel"],"providers":[{"url":"https://www.esri.com/","name":"Esri","roles":["licensor"]},{"url":"https://www.impactobservatory.com/","name":"Impact Observatory","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"raster:bands":[{"nodata":0,"spatial_resolution":10}]},"description":"Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory’s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Global land cover data","file:values":[{"values":[0],"summary":"No Data"},{"values":[1],"summary":"Water"},{"values":[2],"summary":"Trees"},{"values":[4],"summary":"Flooded vegetation"},{"values":[5],"summary":"Crops"},{"values":[7],"summary":"Built area"},{"values":[8],"summary":"Bare ground"},{"values":[9],"summary":"Snow/ice"},{"values":[10],"summary":"Clouds"},{"values":[11],"summary":"Rangeland"}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"io-land-cover","msft:container":"io-lulc","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/label/v1.0.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Global land cover information with 9 classes for 2017-2023 at 10m resolution"},{"id":"goes-cmi","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi"},{"rel":"license","href":"https://www.nesdisia.noaa.gov/policy.html","title":"Public Domain"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi/queryables"},{"rel":"preview","href":"https://planetarycomputer.microsoft.com/api/data/v1/collection/map?collection=goes-cmi","title":"Map of collection mosaic","type":"text/html"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/goes-cmi","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"GOES-R Cloud & Moisture Imagery","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-cmi-thumb.png","title":"GOES CMIP","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/goes-cmi.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"W-MON"},"table:storage_options":{"account_name":"pcstacitems"}},"tilejson":{"title":"Mosaic TileJSON with default rendering","href":"https://planetarycomputer.microsoft.com/api/data/v1/collection/tilejson.json?collection=goes-cmi&expression=C02_2km_wm%3B0.45%2AC02_2km_wm%2B0.1%2AC03_2km_wm%2B0.45%2AC01_2km_wm%3BC01_2km_wm&nodata=-1&rescale=1%2C1000&color_formula=Gamma+RGB+2.5+Saturation+1.4+Sigmoidal+RGB+2+0.7&asset_as_band=True&resampling=bilinear&format=png","type":"application/json","roles":["tiles"]}},"extent":{"spatial":{"bbox":[[-180.0,-81.33,6.3,81.33],[141.7,-81.33,180.0,81.33]]},"temporal":{"interval":[["2017-02-28T00:16:52Z",null]]}},"license":"proprietary","keywords":["GOES","NOAA","NASA","Satellite","Cloud","Moisture"],"providers":[{"url":"https://www.nasa.gov/content/goes","name":"NASA","roles":["producer"]},{"url":"https://www.goes-r.gov/","name":"NOAA","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["GOES-16","GOES-17","GOES-18","GOES-19"],"goes:mode":["3","4","6"],"instruments":["ABI"],"goes:image-type":["FULL DISK","CONUS","MESOSCALE"],"goes:processing-level":["L2"]},"description":"The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis.  Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner’s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users’ Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n","item_assets":{"C01_1km":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Cloud and Moisture Imagery reflectance factor - 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[Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [“Flattening Gamma: Radiometric Terrain Correction for SAR Imagery”](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n","item_assets":{"hh":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","description":"Terrain-corrected gamma naught values of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.","raster:bands":[{"nodata":-32768,"data_type":"float32","spatial_resolution":10}]},"hv":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"HV: horizontal transmit, vertical receive","description":"Terrain-corrected gamma naught values of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.","raster:bands":[{"nodata":-32768,"data_type":"float32","spatial_resolution":10}]},"vh":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VH: vertical transmit, horizontal receive","description":"Terrain-corrected gamma naught values of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied.","raster:bands":[{"nodata":-32768,"data_type":"float32","spatial_resolution":10}]},"vv":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VV: vertical transmit, vertical receive","description":"Terrain-corrected gamma naught values of signal transmitted with vertical polarization and received with vertical polarization with radiometric terrain correction applied.","raster:bands":[{"nodata":-32768,"data_type":"float32","spatial_resolution":10}]}},"stac_version":"1.0.0","msft:group_id":"sentinel-1","msft:container":"sentinel1-grd-rtc","stac_extensions":["https://stac-extensions.github.io/sar/v1.0.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"sentinel1euwestrtc","msft:requires_account":true,"msft:short_description":"Radiometrically terrain corrected SAR imagery derived from the Sentinel 1 Level 1 GRD product.","msft:region":"westeurope"},{"id":"noaa-hrrr","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-hrrr/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-hrrr"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"CC-BY-4.0 license"},{"rel":"documentation","href":"https://rapidrefresh.noaa.gov/hrrr/","type":"text/html","title":"NOAA HRRR documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-hrrr","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-hrrr/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-hrrr","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA High Resolution Rapid Refresh (HRRR)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/hrrr_small.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"NOAA HRRR"}},"extent":{"spatial":{"bbox":[[-174.8849,21.122192719272277,-60.891244531606546,76.3464],[-134.12142793280145,21.122192719272277,-60.891244531606546,52.62870335266725],[-174.8849,41.596,-115.6988,76.3464]]},"temporal":{"interval":[["2021-03-21T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["NOAA","HRRR","forecast","atmospheric","weather"],"providers":[{"url":"https://www.noaa.gov/","name":"NOAA","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"The [High-Resolution Rapid Refresh (HRRR)](https://rapidrefresh.noaa.gov/hrrr/) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model. 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The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n","item_assets":{"metadata":{"type":"text/html","roles":["metadata"],"title":"Metadata"},"burn-severity":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Burn severity","file:values":[{"values":[0],"summary":"No Data"},{"values":[1],"summary":"Unburned to low"},{"values":[2],"summary":"Low"},{"values":[3],"summary":"Moderate"},{"values":[4],"summary":"High"},{"values":[5],"summary":"Increased Greenness"}]}},"stac_version":"1.0.0","msft:container":"cpdata","stac_extensions":["https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"cpdataeuwest","msft:short_description":"Annual burn severity mosaics for the continental United States and Alaska","msft:region":"westeurope"},{"id":"noaa-c-cap","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-c-cap/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-c-cap"},{"rel":"describedby","href":"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-product-page.pdf","type":"application/pdf","title":"Handout - C-CAP Products"},{"rel":"describedby","href":"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-faq-regional.pdf","type":"application/pdf","title":"Frequently Asked Questions"},{"rel":"describedby","href":"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-class-scheme-regional.pdf","type":"application/pdf","title":"C-CAP Classification Scheme and Class Definitions"},{"rel":"license","href":"https://coast.noaa.gov/digitalcoast/data/ccapregional.html","type":"text/html","title":"C-CAP Regional Land Cover and Change"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-c-cap/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-c-cap","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"C-CAP Regional Land Cover and Change","assets":{"boundary":{"href":"https://coast.noaa.gov/data/digitalcoast/zip/ccap-mapping-bndry-wgs84.zip","type":"application/zip","title":"C-CAP Mapping Boundary (shp file)"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-c-cap.png","type":"image/png","roles":["data"],"title":"NOAA C-CAP"},"geoparquet-items":{"href":"abfs://items/noaa-c-cap.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-160.315986,17.892786,-64.966857,49.471148],[-127.984554,22.718719,-64.966857,49.471148],[-160.315986,18.879215,-154.706564,22.271167],[-67.290869,17.892786,-65.230246,18.538629]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2016-12-31T00:00:00Z"],["1975-01-01T00:00:00Z","2016-12-31T00:00:00Z"],["1992-01-01T00:00:00Z","2005-12-31T00:00:00Z"],["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"license":"proprietary","keywords":["Land Cover","Land Use","NOAA","Coastal"],"providers":[{"url":"https://coast.noaa.gov/digitalcoast/data/ccapregional.html","name":"National Oceanic and Atmospheric Administration, Office for Coastal Management","roles":["licensor","producer","processor"]},{"url":"https//planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S.  Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery.  Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another.  The use of standardized data and procedures assures consistency through time and across geographies.  C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure.  The data are updated every 5 years.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"value":0,"description":"Background"},{"value":1,"description":"Unclassified (Cloud, Shadow, etc)"},{"value":2,"color-hint":"FFFFFF","description":"High Intensity Developed"},{"value":3,"color-hint":"C1C1C1","description":"Medium Intensity Developed"},{"value":4,"color-hint":"979797","description":"Low Intensity Developed"},{"value":5,"color-hint":"CCCA42","description":"Developed Open Space"},{"value":6,"color-hint":"4E2409","description":"Cultivated Crops"},{"value":7,"color-hint":"BCA05B","description":"Pasture/Hay"},{"value":8,"color-hint":"EABB8E","description":"Grassland/Herbaceous"},{"value":9,"color-hint":"6FEC48","description":"Deciduous Forest"},{"value":10,"color-hint":"15380A","description":"Evergreen Forest"},{"value":11,"color-hint":"489D48","description":"Mixed Forest"},{"value":12,"color-hint":"6D6C1F","description":"Scrub/Shrub"},{"value":13,"color-hint":"245A5A","description":"Palustrine Forested Wetland"},{"value":14,"color-hint":"E2742D","description":"Palustrine Scrub/Shrub Wetland"},{"value":15,"color-hint":"DE3BEA","description":"Palustrine Emergent Wetland (Persistent)"},{"value":16,"color-hint":"37083B","description":"Estuarine Forested Wetland"},{"value":17,"color-hint":"631569","description":"Estuarine Scrub/Shrub Wetland"},{"value":18,"color-hint":"A028A9","description":"Estuarine Emergent Wetland"},{"value":19,"color-hint":"6DEEF0","description":"Unconsolidated Shore"},{"value":20,"color-hint":"F3F050","description":"Bare Land"},{"value":21,"color-hint":"000A72","description":"Open Water"},{"value":22,"color-hint":"0021E8","description":"Palustrine Aquatic Bed"},{"value":23,"color-hint":"6777D4","description":"Estuarine Aquatic Bed"},{"value":24,"color-hint":"F3D147","description":"Tundra"},{"value":25,"color-hint":"BBF7EF","description":"Perennial Ice/Snow"}]},"metadata":{"type":"application/xml","roles":["metadata"]}},"sci:citation":"National Oceanic and Atmospheric Administration, Office for Coastal Management. Name of Data Set: Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed April 2022 at www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/.","stac_version":"1.0.0","msft:container":"noaa-c-cap","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Periodic coastal landcover classifications back to 1975","msft:region":"westeurope"},{"id":"3dep-lidar-copc","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-copc/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-copc"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-copc/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Point Cloud","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-copc-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"Thumbnail"}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","Point cloud"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[2.0]},"description":"This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).","item_assets":{"data":{"type":"application/vnd.laszip+copc","roles":["data"],"title":"COPC data","pc:type":"lidar","pc:encoding":"application/vnd.laszip+copc"},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COPC"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-copc","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/pointcloud/v1.0.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"Nationwide Lidar point cloud data in COPC format.","msft:region":"westeurope"},{"id":"modis-64A1-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-64A1-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-64A1-061"},{"rel":"help","href":"https://lpdaac.usgs.gov/documents/1006/MCD64_User_Guide_V61.pdf","title":"MCD64 User Guide"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD64A1","title":"MCD64A1 file specification"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD64A1.061","title":"LP DAAC - MCD64A1"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"LP DAAC - Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-64A1-061/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/modis-64A1-061","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MODIS Burned Area Monthly","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-64A1-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Burned Area Monthly thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-64A1-061.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["NASA","MODIS","Satellite","Imagery","Global","Fire","MCD64A1"],"providers":[{"url":"https://lpdaac.usgs.gov/","name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["aqua","terra"],"instruments":["modis"]},"description":"The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.","item_assets":{"QA":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Quality Assurance Indicators","raster:bands":[{"data_type":"uint8","spatial_resolution":500}]},"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"Last_Day":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Last day of the year of reliable change detection","raster:bands":[{"unit":"Day","data_type":"int16","spatial_resolution":500}]},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"Burn_Date":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Burn day of year","raster:bands":[{"unit":"Day","data_type":"int16","spatial_resolution":500}]},"First_Day":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"First day of the year of reliable change detection","raster:bands":[{"unit":"Day","data_type":"int16","spatial_resolution":500}]},"Burn_Date_Uncertainty":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Estimated uncertainty in burn day","raster:bands":[{"unit":"Day","data_type":"uint8","spatial_resolution":500}]}},"stac_version":"1.0.0","msft:group_id":"modis","msft:container":"modis-061","stac_extensions":["https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.5067/MODIS/MCD64A1.061","citation":"Giglio, L., Justice, C., Boschetti, L., &amp; Roy, D. (2021). <i>MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.061"}],"msft:storage_account":"modiseuwest","msft:short_description":"MODIS Burned Area Monthly","msft:region":"westeurope"},{"id":"alos-fnf-mosaic","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-fnf-mosaic/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-fnf-mosaic"},{"rel":"handbook","href":"https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf","type":"application/pdf","title":"Global 25 m Resolution PALSAR-2/PALSAR Forest/Non-Forest Map (FNF)","description":"Also includes data usage information"},{"rel":"license","href":"https://earth.jaxa.jp/policy/en.html","type":"text/html","title":"JAXA Terms of Use of Research Data"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-fnf-mosaic/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/alos-fnf-mosaic","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ALOS Forest/Non-Forest Annual Mosaic","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/alos-fnf-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"ALOS PALSAR MOSAIC"},"geoparquet-items":{"href":"abfs://items/alos-fnf-mosaic.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,85.0,180.0,-56.0]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"proprietary","version":"2.0.0","keywords":["ALOS","JAXA","Forest","Land Cover","Global"],"providers":[{"url":"https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm","name":"Japan Aerospace Exploration Agency","roles":["producer","processor","licensor"]},{"url":"https://developmentseed.org","name":"Development Seed","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft Planetary Computer","roles":["host","processor"]}],"summaries":{"platform":["ALOS","ALOS-2"],"instruments":["PALSAR","PALSAR-2"]},"description":"The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n","item_assets":{"C":{"role":"data","type":"image/tiff; application=geotiff; profile=cloud-optimized","title":"C","description":"Forest vs Non-Forest classification","raster:bands":[{"nodata":0,"data_type":"uint8"}],"classification:classes":[{"name":"no_data","value":0,"description":"No data"},{"name":"forest (>90%)","value":1,"description":"Forest (>90% canopy cover)"},{"name":"forest (10-90%)","value":2,"description":"Forest (10-90% canopy cover)"},{"name":"non_forest","value":3,"description":"Non-forest"},{"name":"water","value":4,"description":"Water"}]}},"stac_version":"1.0.0","msft:container":"palsar","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/sar/v1.0.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/version/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"https://pceo.blob.core.windows.net/","msft:short_description":"Global 25m resolution map classifying \"forest\" and \"non-forest\" landcover.","msft:region":"westeurope"},{"id":"3dep-lidar-returns","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-returns/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-returns"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-returns/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-returns","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Returns","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/numberofreturns-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"},"geoparquet-items":{"href":"abfs://items/3dep-lidar-returns.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","NumberOfReturns"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[5.0]},"description":"This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"COG data","raster:bands":[{"name":"ReturnNumber","unit":"metre","sampling":"point","data_type":"int16","description":"Raster for ReturnNumber PDAL dimension"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json#","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the ReturnNumber dimension.","msft:region":"westeurope"},{"id":"mobi","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi"},{"rel":"license","href":"https://aka.ms/mobi-license","title":"NatureServe MoBI license"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/mobi","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MoBI: Map of Biodiversity Importance","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/mobi.png","type":"image/png","roles":["thumbnail"],"title":"MoBI"},"geoparquet-items":{"href":"abfs://items/mobi.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-130.23751729209377,21.736663926419077,-63.66198266211968,49.184200486300604]]},"temporal":{"interval":[["2020-04-14T00:00:00Z","2020-04-14T00:00:00Z"]]}},"license":"proprietary","keywords":["MoBI","Natureserve","United States","Biodiversity"],"providers":[{"url":"http://natureserve.org/","name":"NatureServe","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[990]},"description":"The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity.  For each type, this data set includes five different layers &ndash; one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) &ndash; for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n","item_assets":{"RSR_All":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Range-size rarity for all species"},"RSR_Plants":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Range-size rarity for vascular plants"},"RSR_Vertebrates":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Range-size rarity for vertebrates"},"RSR_AquaticInverts":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Range-size rarity for aquatic invertebrates"},"PWRSR_GAP12_SUM_All":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Protection-weighted range-size rarity for 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Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n","item_assets":{"qa":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Surface Temperature Quality Assessment Band","description":"Collection 2 Level-2 Quality Assessment Band (ST_QA) Surface Temperature Product","raster:bands":[{"unit":"kelvin","scale":0.01,"nodata":-9999,"data_type":"int16","spatial_resolution":30}]},"ang":{"type":"text/plain","roles":["metadata"],"title":"Angle Coefficients File","description":"Collection 2 Level-1 Angle Coefficients File"},"red":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","reflectance"],"title":"Red Band","eo:bands":[{"common_name":"red","description":"Visible red"}],"raster:bands":[{"scale":0.0000275,"nodata":0,"offset":-0.2,"data_type":"uint16","spatial_resolution":30}]},"blue":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","reflectance"],"title":"Blue Band","eo:bands":[{"common_name":"blue","description":"Visible blue"}],"raster:bands":[{"scale":0.0000275,"nodata":0,"offset":-0.2,"data_type":"uint16","spatial_resolution":30}]},"drad":{"type":"image/tiff; 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It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft’s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n","item_assets":{"biomass":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Annual estimates of aboveground woody biomass.","raster:bands":[{"unit":"tonnes","nodata":2147483647,"data_type":"uint32"}]},"biomass_wm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Annual estimates of aboveground woody biomass (Web Mercator).","raster:bands":[{"unit":"tonnes","nodata":2147483647,"data_type":"uint32"}]},"biomass_change":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Annual estimates of changes (gains and losses) in aboveground woody biomass from the previous year.","raster:bands":[{"unit":"tonnes","nodata":-32768,"data_type":"int16"}]},"biomass_change_wm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Annual estimates of changes (gains and losses) in aboveground woody biomass from the previous year (Web Mercator).","raster:bands":[{"unit":"tonnes","nodata":-32768,"data_type":"int16"}]}},"stac_version":"1.0.0","msft:container":"chloris-biomass","stac_extensions":["https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems during the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.","msft:region":"westeurope"},{"id":"kaza-hydroforecast","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast"},{"rel":"license","href":"https://cdla.dev/sharing-1-0/","type":"text/html","title":"Community Data License Agreement – Sharing, Version 1.0"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/kaza-hydroforecast","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"HydroForecast - Kwando & Upper Zambezi Rivers","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/kaza-hydroforecast-thumbnail.png","type":"image/png","title":"Thumbnail"}},"extent":{"spatial":{"bbox":[[21.04494,-17.792517,23.343421,-13.08062],[23.343421,-17.792517,23.343421,-17.792517],[23.343421,-17.792517,23.343421,-17.792517],[21.04494,-15.13158,21.04494,-15.13158],[21.80157,-16.01209,21.80157,-16.01209],[22.91715,-17.38856,22.91715,-17.38856],[23.00610530305009,-17.347398048598034,23.00610530305009,-17.347398048598034],[22.669092,-14.971843,22.669092,-14.971843],[22.675487,-13.08062,22.675487,-13.08062]]},"temporal":{"interval":[["2022-01-01T00:00:00Z",null]]}},"license":"CDLA-Sharing-1.0","version":"1.0.0","keywords":["Water","HydroForecast","Streamflow","Hydrology","Upstream Tech"],"providers":[{"url":"https://www.upstream.tech","name":"Upstream Tech","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m³/s)\n    \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n    * Zambezi at Chavuma\n    * Luanginga at Kalabo\n* Kwando basin sites\n    * Kwando at Kongola -- total basin flows\n    * Kwando Sub-basin 1\n    * Kwando Sub-basin 2 \n    * Kwando Sub-basin 3\n    * Kwando Sub-basin 4\n    * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Forecast Data","description":"All seasonal river flow forecasts for a specific forecast site, produced by Upstream Tech's HydroForecast model"}},"stac_version":"1.0.0","table:columns":[{"name":"initialization_time","type":"timestamp","description":"The initial timestamp in the forecast model which created this forecast step. All rows with the same initialization time are part of the same \"forecast\" and are produced at the same time."},{"name":"valid_time","type":"timestamp","description":"The point in time this forecast step is predicting. valid_time = initialization_time + lead_time_hours. The discharge values in this row are the average rate from this time (inclusive) until the next valid time (exclusive)."},{"name":"lead_time_hours","type":"int64","description":"The number of hours ahead of the initialization time this forecast step is predicting."},{"name":"discharge_mean","type":"double","description":"The mean of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_median","type":"double","description":"The median of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.01","type":"double","description":"The quantile 0.01 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.025","type":"double","description":"The quantile 0.025 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.05","type":"double","description":"The quantile 0.05 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.1","type":"double","description":"The quantile 0.1 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.25","type":"double","description":"The quantile 0.25 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.5","type":"double","description":"The quantile 0.5 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.75","type":"double","description":"The quantile 0.75 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.9","type":"double","description":"The quantile 0.9 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.95","type":"double","description":"The quantile 0.95 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.975","type":"double","description":"The quantile 0.975 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."},{"name":"discharge_q0.99","type":"double","description":"The quantile 0.99 of the probabilistic streamflow prediction, as an average rate across the forecast step in m³/s."}],"msft:container":"hydroforecast","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Seasonal river flow forecasts for the Kwando and Upper Zambezi rivers in the KAZA region of Africa, produced by Upstream Tech's HydroForecast model.","msft:region":"westeurope"},{"id":"planet-nicfi-analytic","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic"},{"rel":"license","href":"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf","type":"application/pdf","title":"Participant License Agreement."},{"rel":"documentation","href":"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf","type":"application/pdf","title":"Participant License Agreement."},{"rel":"documentation","href":"https://www.planet.com/nicfi/","type":"text/html","title":"NICFI Program - 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Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway’s International Climate & Forests Initiative (NICFI), users can access Planet’s high-resolution, analysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI’s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world’s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. 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The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. 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NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD17A2H.061"}],"msft:storage_account":"modiseuwest","msft:short_description":"MODIS Gross Primary Productivity 8-Day","msft:region":"westeurope"},{"id":"modis-11A2-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-11A2-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-11A2-061"},{"rel":"help","href":"https://lpdaac.usgs.gov/documents/715/MOD11_User_Guide_V61.pdf","title":"MOD11 User Guide"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD11A2","title":"MOD11A2 file specification"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD11A2","title":"MYD11A2 file specification"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD11A2.061","title":"LP DAAC - MOD11A2"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD11A2.061","title":"LP DAAC - MYD11A2"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"LP DAAC - Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-11A2-061/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/modis-11A2-061","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MODIS Land Surface Temperature/Emissivity 8-Day","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-11A2-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Land Surface Temperature/Emissivity 8-Day thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-11A2-061.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"license":"proprietary","keywords":["NASA","MODIS","Satellite","Global","Temperature","MOD11A2","MYD11A2"],"providers":[{"url":"https://lpdaac.usgs.gov/","name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["aqua","terra"],"instruments":["modis"]},"description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. 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[Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n","sci:citation":"Thornton, M.M., R. Shrestha, P.E. Thornton, S. Kao, Y. Wei, and B.E. Wilson. 2021. Daymet Version 4 Monthly Latency: Daily Surface Weather Data. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1904","stac_version":"1.0.0","msft:group_id":"daymet","cube:variables":{"vp":{"type":"data","unit":"Pa","attrs":{"units":"Pa","long_name":"daily average vapor pressure","cell_methods":"area: mean time: mean","grid_mapping":"lambert_conformal_conic"},"shape":[14965,231,364],"chunks":[365,231,364],"dimensions":["time","y","x"],"description":"daily average vapor pressure"},"lat":{"type":"auxiliary","unit":"degrees_north","attrs":{"units":"degrees_north","long_name":"latitude coordinate","standard_name":"latitude"},"shape":[231,364],"chunks":[231,364],"dimensions":["y","x"],"description":"latitude coordinate"},"lon":{"type":"auxiliary","unit":"degrees_east","attrs":{"units":"degrees_east","long_name":"longitude coordinate","standard_name":"longitude"},"shape":[231,364],"chunks":[231,364],"dimensions":["y","x"],"description":"longitude 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rico"],"stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"daymeteuwest","msft:short_description":"Daily surface weather data on a 1-km grid for Puerto Rico","msft:region":"westeurope"},{"id":"3dep-lidar-dtm-native","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm-native","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Digital Terrain Model (Native)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-dtm-native-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","DTM"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[2.0]},"description":"This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"COG data","raster:bands":[{"name":"Z","unit":"metre","sampling":"point","data_type":"float32","description":"Raster for Z PDAL Dimension"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json#"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs (COGs) using vendor provided ground classification.","msft:region":"westeurope"},{"id":"3dep-lidar-classification","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-classification","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Classification","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-classification-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","Classification"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[5.0]},"description":"This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"COG data","raster:bands":[{"name":"Classification","unit":"metre","sampling":"point","data_type":"int16","description":"Raster for Classification PDAL dimension, ASPRS Lidar Point Classification Standard"}],"classification:classes":[{"value":0,"description":"Never Classified"},{"value":1,"description":"Unclassified"},{"value":2,"description":"Ground"},{"value":3,"description":"Low Vegetation"},{"value":4,"description":"Medium Vegetation"},{"value":5,"description":"High Vegetation"},{"value":6,"description":"Building"},{"value":9,"description":"Water"},{"value":10,"description":"Rail"},{"value":11,"description":"Road"},{"value":17,"description":"Bridge Deck"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for Cloud Optimized Geotiffs (COGs) created according to PDAL's Intensity dimension","msft:region":"westeurope"},{"id":"3dep-lidar-dtm","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm"},{"rel":"license","href":"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services","title":"About 3DEP Products & Services"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS 3DEP Lidar Digital Terrain Model","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dtm-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"},"geoparquet-items":{"href":"abfs://items/3dep-lidar-dtm.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","3DEP","COG","DTM"],"providers":[{"name":"Landrush","roles":["processor","producer"]},{"url":"https://www.usgs.gov/core-science-systems/ngp/3dep/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[2.0]},"description":"This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"COG data","raster:bands":[{"type":"Z","unit":"metre","sampling":"point","data_type":"float32","description":"Z PDAL Dimension"}]},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"3DEP Lidar COG"}},"stac_version":"1.0.0","msft:group_id":"3dep-lidar","msft:container":"usgs-3dep-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json#","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"usgslidareuwest","msft:short_description":"3DEP Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs (COGs) using PDAL's filters.smrf.","msft:region":"westeurope"},{"id":"gap","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap"},{"rel":"license","href":"https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits","title":"Public Domain"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/gap","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS Gap Land Cover","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png","type":"image/png","roles":["data"],"title":"USGS GAP"},"geoparquet-items":{"href":"abfs://items/gap.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}},"original-data-ak":{"href":"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_ak.zip","type":"application/zip","roles":["data"],"title":"Alaska source data","description":"Original source data for Alaska"},"original-data-hi":{"href":"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_hi.zip","type":"application/zip","roles":["data"],"title":"Hawaii source data","description":"Original source data for Hawaii"},"original-data-pr":{"href":"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/pr_landcover.zip","type":"application/zip","roles":["data"],"title":"Puerto Rico source data","description":"Original source data for Puerto Rico"},"original-data-conus":{"href":"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gap_landfire_nationalterrestrialecosystems2011.zip","type":"application/zip","roles":["data"],"title":"CONUS source data","description":"Original source data for the continental United States (CONUS)"}},"extent":{"spatial":{"bbox":[[-127.9710481801793,22.797789263564383,-65.26634281147894,51.64692620669362],[-178.13166387448902,49.09079265233118,179.87849702345594,71.43382483774205],[-160.26640694607218,18.851824447510786,-154.66974350173518,22.295114188194738],[-67.9573345827195,17.874066536543,-65.21836408976736,18.5296513469496]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2011-12-31T00:00:00Z"]]}},"license":"proprietary","keywords":["USGS","GAP","LANDFIRE","Land Cover","United States"],"providers":[{"url":"https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"gsd":[30],"label:classes":[{"classes":["0","South Florida Bayhead Swamp","South Florida Cypress Dome","South Florida Dwarf Cypress Savanna","South Florida Mangrove Swamp","South Florida Hardwood Hammock","Southeast Florida Coastal Strand and Maritime Hammock","Southwest Florida Coastal Strand and Maritime Hammock","South Florida Pine Rockland","Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory","Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory","Atlantic Coastal Plain Upland Longleaf Pine Woodland","Atlantic Coastal Plain Xeric River Dune","East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier","East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier","Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier","Florida Longleaf Pine Sandhill- Open Understory Modifier","West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland","Atlantic Coastal Plain Central Maritime Forest","Atlantic Coastal Plain Southern Maritime Forest","Central and South Texas Coastal Fringe Forest and Woodland","East Gulf Coastal Plain Limestone Forest","East Gulf Coastal Plain Maritime Forest","East Gulf Coastal Plain Southern Loess Bluff Forest","East Gulf Coastal Plain Southern Mesic Slope Forest","Mississippi Delta Maritime Forest","Southern Coastal Plain Dry Upland Hardwood Forest","Southern Coastal Plain Oak Dome and Hammock","West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland","West Gulf Coastal Plain Mesic Hardwood Forest","East-Central Texas Plains Pine Forest and Woodland","West Gulf Coastal Plain Pine-Hardwood Forest","West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland","Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier","Deciduous Plantations","East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier","East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier","East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier","Evergreen Plantation or Managed Pine","California Central Valley Mixed Oak Savanna","California Coastal Closed-Cone Conifer Forest and Woodland","California Coastal Live Oak Woodland and Savanna","California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna","Central and Southern California Mixed Evergreen Woodland","Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland","Southern California Oak Woodland and Savanna","Madrean Encinal","Madrean Pinyon-Juniper Woodland","Madrean Pine-Oak Forest and Woodland","Madrean Upper Montane Conifer-Oak Forest and Woodland","Edwards Plateau Dry-Mesic Slope Forest and Woodland","Edwards Plateau Limestone Savanna and Woodland","Edwards Plateau Mesic Canyon","Llano Uplift Acidic Forest, Woodland and Glade","East Cascades Oak-Ponderosa Pine Forest and Woodland","Mediterranean California Mixed Evergreen Forest","Mediterranean California Mixed Oak Woodland","North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland","North Pacific Oak Woodland","Edwards Plateau Limestone Shrubland","Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood","Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier","Central and Southern Appalachian Montane Oak Forest","Central and Southern Appalachian Northern Hardwood Forest","Central Appalachian Oak and Pine Forest","Crosstimbers Oak Forest and Woodland","East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier","East Gulf Coastal Plain Northern Dry Upland Hardwood Forest","East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier","East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier","East-Central Texas Plains Post Oak Savanna and Woodland","Lower Mississippi River Dune Woodland and Forest","Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest","North-Central Interior Dry Oak Forest and Woodland","North-Central Interior Dry-Mesic Oak Forest and Woodland","Northeastern Interior Dry Oak Forest - Mixed Modifier","Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier","Northeastern Interior Dry Oak Forest-Hardwood Modifier","Northeastern Interior Dry-Mesic Oak Forest","Northern Atlantic Coastal Plain Dry Hardwood Forest","Crowley's Ridge Sand Forest","Ouachita Montane Oak Forest","Ozark-Ouachita Dry Oak Woodland","Ozark-Ouachita Dry-Mesic Oak Forest","Southern and Central Appalachian Oak Forest","Southern and Central Appalachian Oak Forest - Xeric","Southern Interior Low Plateau Dry-Mesic Oak Forest","Southern Ridge and Valley Dry Calcareous Forest","Southern Ridge and Valley Dry Calcareous Forest - Pine modifier","East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier","Managed Tree Plantation","Ruderal forest","Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier","Acadian Low-Elevation Spruce-Fir-Hardwood Forest","Acadian-Appalachian Montane Spruce-Fir Forest","Appalachian Hemlock-Hardwood Forest","Central and Southern Appalachian Spruce-Fir Forest","0","Laurentian-Acadian Northern Hardwoods Forest","Laurentian-Acadian Northern Pine-(Oak) Forest","Laurentian-Acadian Pine-Hemlock-Hardwood Forest","Paleozoic Plateau Bluff and Talus","Southern Appalachian Northern Hardwood Forest","Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest","Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood","East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier","East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier","Ozark-Ouachita Shortleaf Pine-Bluestem Woodland","Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland","Southeastern Interior Longleaf Pine Woodland","Southern Appalachian Low Mountain Pine Forest","Southern Piedmont Dry Oak-(Pine) Forest","Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier","Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier","Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier","Eastern Great Plains Tallgrass Aspen Parkland","Northwestern Great Plains Aspen Forest and Parkland","Northwestern Great Plains Shrubland","Western Great Plains Dry Bur Oak Forest and Woodland","Western Great Plains Wooded Draw and Ravine","Southern Atlantic Coastal Plain Mesic Hardwood Forest","East Gulf Coastal Plain Northern Loess Bluff Forest","East Gulf Coastal Plain Northern Mesic Hardwood Forest","North-Central Interior Beech-Maple Forest","North-Central Interior Maple-Basswood Forest","Ozark-Ouachita Mesic Hardwood Forest","South-Central Interior Mesophytic Forest","Southern and Central Appalachian Cove Forest","Crowley's Ridge Mesic Loess Slope Forest","Southern Piedmont Mesic Forest","Appalachian Shale Barrens","Atlantic Coastal Plain Northern Maritime Forest","Laurentian Pine-Oak Barrens","Northeastern Interior Pine Barrens","Northern Atlantic Coastal Plain Pitch Pine Barrens","Southern Appalachian Montane Pine Forest and Woodland","East Cascades Mesic Montane Mixed-Conifer Forest and Woodland","Middle Rocky Mountain Montane Douglas-fir Forest and Woodland","Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest","Northern Rocky Mountain Foothill Conifer Wooded Steppe","Northern Rocky Mountain Mesic Montane Mixed Conifer Forest","Northern Rocky Mountain Ponderosa Pine Woodland and Savanna","Northern Rocky Mountain Western Larch Savanna","Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna","Rocky Mountain Foothill Limber Pine-Juniper Woodland","Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland","Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland","Northern Rocky Mountain Subalpine Woodland and Parkland","Rocky Mountain Aspen Forest and Woodland","Rocky Mountain Lodgepole Pine Forest","Rocky Mountain Poor-Site Lodgepole Pine Forest","Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland","Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland","Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland","Rocky Mountain Bigtooth Maple Ravine Woodland","Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland","Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland","Southern Rocky Mountain Ponderosa Pine Savanna","Southern Rocky Mountain Ponderosa Pine Woodland","California Montane Jeffrey Pine-(Ponderosa Pine) Woodland","Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland","Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland","Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland","Mediterranean California Mesic Mixed Conifer Forest and Woodland","Sierran-Intermontane Desert Western White Pine-White Fir Woodland","California Coastal Redwood Forest","North Pacific Broadleaf Landslide Forest and Shrubland","North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest","North Pacific Hypermaritime Sitka Spruce Forest","North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest","North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland","North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest","North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest","North Pacific Mesic Western Hemlock-Silver Fir Forest","North Pacific Wooded Volcanic Flowage","Mediterranean California Red Fir Forest","Mediterranean California Subalpine Woodland","North Pacific Maritime Mesic Subalpine Parkland","North Pacific Mountain Hemlock Forest","Northern California Mesic Subalpine Woodland","Northern Pacific Mesic Subalpine Woodland","Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland","Columbia Plateau Western Juniper Woodland and Savanna","Great Basin Pinyon-Juniper Woodland","Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland","Inter-Mountain Basins Juniper Savanna","Colorado Plateau Pinyon-Juniper Shrubland","Colorado Plateau Pinyon-Juniper Woodland","Southern Rocky Mountain Juniper Woodland and Savanna","Southern Rocky Mountain Pinyon-Juniper Woodland","Northwestern Great Plains Floodplain","Northwestern Great Plains Riparian","Western Great Plains Floodplain","Western Great Plains Floodplain Systems","Western Great Plains Riparian Woodland and Shrubland","Central Appalachian Floodplain - Forest Modifier","Central Appalachian Riparian - Forest Modifier","Central Interior and Appalachian Floodplain Systems","Central Interior and Appalachian Riparian Systems","Laurentian-Acadian Floodplain Systems","Ozark-Ouachita Riparian","South-Central Interior Large Floodplain","South-Central Interior Large Floodplain - Forest Modifier","South-Central Interior Small Stream and Riparian","North-Central Interior and Appalachian Rich Swamp","0","0","Laurentian-Acadian Swamp Systems","North-Central Interior Wet Flatwoods","0","South-Central Interior / Upper Coastal Plain Wet Flatwoods","0","Southern Piedmont/Ridge and Valley Upland Depression Swamp","Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier","Atlantic Coastal Plain Brownwater Stream Floodplain Forest","Atlantic Coastal Plain Northern Tidal Wooded Swamp","Atlantic Coastal Plain Small Blackwater River Floodplain Forest","Atlantic Coastal Plain Small Brownwater River Floodplain Forest","Atlantic Coastal Plain Southern Tidal Wooded Swamp","East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier","East Gulf Coastal Plain Small Stream and River Floodplain Forest","East Gulf Coastal Plain Tidal Wooded Swamp","0","Southeastern Great Plains Riparian Forest","Southeastern Great Plains Floodplain Forest","Mississippi River Bottomland Depression","Mississippi River Floodplain and Riparian Forest","Mississippi River Low Floodplain (Bottomland) Forest","Mississippi River Riparian Forest","Red River Large Floodplain Forest","Southern Coastal Plain Blackwater River Floodplain Forest","Southern Piedmont Large Floodplain Forest - Forest Modifier","Southern Piedmont Small Floodplain and Riparian Forest","West Gulf Coastal Plain Large River Floodplain Forest","West Gulf Coastal Plain Near-Coast Large River Swamp","West Gulf Coastal Plain Small Stream and River Forest","Atlantic Coastal Plain Streamhead Seepage Swamp -  Pocosin -  and Baygall","Gulf and Atlantic Coastal Plain Swamp Systems","Southern Coastal Plain Hydric Hammock","Southern Coastal Plain Seepage Swamp and Baygall","West Gulf Coastal Plain Seepage Swamp and Baygall","Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest  - Taxodium/Nyssa Modifier","Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier","East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods","Lower Mississippi River Bottomland Depressions - Forest Modifier","Lower Mississippi River Flatwoods","Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest","Southern Coastal Plain Nonriverine Basin Swamp","Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier","Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier","Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier","West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods","West Gulf Coastal Plain Pine-Hardwood Flatwoods","Edwards Plateau Riparian","Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland","Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland","Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods","Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods","Central Florida Pine Flatwoods","East Gulf Coastal Plain Near-Coast Pine Flatwoods","East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier","East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier","South Florida Pine Flatwoods","Southern Coastal Plain Nonriverine Cypress Dome","West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods","Columbia Basin Foothill Riparian Woodland and Shrubland","Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland","0","Northern Rocky Mountain Conifer Swamp","Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland","Rocky Mountain Lower Montane Riparian Woodland and Shrubland","Rocky Mountain Montane Riparian Systems","Rocky Mountain Subalpine-Montane Riparian Woodland","North Pacific Hardwood-Conifer Swamp","North Pacific Lowland Riparian Forest and Shrubland","North Pacific Montane Riparian Woodland and Shrubland","North Pacific Shrub Swamp","California Central Valley Riparian Woodland and Shrubland","Mediterranean California Foothill and Lower Montane Riparian Woodland","Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep","North American Warm Desert Lower Montane Riparian Woodland and Shrubland","North American Warm Desert Riparian Systems","North American Warm Desert Riparian Woodland and Shrubland","Tamaulipan Floodplain","Tamaulipan Riparian Systems","Boreal Aspen-Birch Forest","Boreal Jack Pine-Black Spruce Forest","Boreal White Spruce-Fir-Hardwood Forest","Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen","Eastern Boreal Floodplain","South Florida Shell Hash Beach","Southeast Florida Beach","Southwest Florida Beach","South Florida Everglades Sawgrass Marsh","South Florida Freshwater Slough and Gator Hole","South Florida Wet Marl Prairie","California Maritime Chaparral","California Mesic Chaparral","California Xeric Serpentine Chaparral","Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral","Mediterranean California Mesic Serpentine Woodland and Chaparral","Northern and Central California Dry-Mesic Chaparral","Southern California Dry-Mesic Chaparral","Southern California Coastal Scrub","California Central Valley and Southern Coastal Grassland","California Mesic Serpentine Grassland","Columbia Basin Foothill and Canyon Dry Grassland","Columbia Basin Palouse Prairie","North Pacific Alpine and Subalpine Dry Grassland","North Pacific Montane Grassland","North Pacific Montane Shrubland","Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland","Northern Rocky Mountain Montane-Foothill Deciduous Shrubland","Northern Rocky Mountain Subalpine Deciduous Shrubland","Northern Rocky Mountain Subalpine-Upper Montane Grassland","Southern Rocky Mountain Montane-Subalpine Grassland","Rocky Mountain Gambel Oak-Mixed Montane Shrubland","Rocky Mountain Lower Montane-Foothill Shrubland","California Northern Coastal Grassland","North Pacific Herbaceous Bald and Bluff","North Pacific Hypermaritime Shrub and Herbaceous Headland","Willamette Valley Upland Prairie and Savanna","Mediterranean California Subalpine Meadow","Rocky Mountain Subalpine-Montane Mesic Meadow","Central Mixedgrass Prairie","Northwestern Great Plains Mixedgrass Prairie","Western Great Plains Foothill and Piedmont Grassland","Western Great Plains Tallgrass Prairie","Western Great Plains Sand Prairie","Western Great Plains Sandhill Steppe","Western Great Plains Mesquite Woodland and Shrubland","Western Great Plains Shortgrass Prairie","Arkansas Valley Prairie and Woodland","Central Tallgrass Prairie","North-Central Interior Oak Savanna","North-Central Interior Sand and Gravel Tallgrass Prairie","North-Central Oak Barrens","Northern Tallgrass Prairie","Southeastern Great Plains Tallgrass Prairie","Texas Blackland Tallgrass Prairie","Texas-Louisiana Coastal Prairie","Central Appalachian Pine-Oak Rocky Woodland","Southern Appalachian Grass and Shrub Bald","Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier","Southern Appalachian Grass and Shrub Bald - Shrub Modifier","Central Appalachian Alkaline Glade and Woodland","Central Interior Highlands Calcareous Glade and Barrens","Central Interior Highlands Dry Acidic Glade and Barrens","Cumberland Sandstone Glade and Barrens","Great Lakes Alvar","Nashville Basin Limestone Glade","Southern Ridge and Valley / Cumberland Dry Calcareous Forest","Southern Piedmont Glade and Barrens","East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier","East Gulf Coastal Plain Jackson Prairie and Woodland","Eastern Highland Rim Prairie and Barrens - Dry Modifier","Coahuilan Chaparral","Madrean Oriental Chaparral","Mogollon Chaparral","Sonora-Mojave Semi-Desert Chaparral","California Montane Woodland and Chaparral","Great Basin Semi-Desert Chaparral","Florida Dry Prairie","Florida Peninsula Inland Scrub","West Gulf Coastal Plain Catahoula Barrens","West Gulf Coastal Plain Nepheline Syenite Glade","East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier","West Gulf Coastal Plain Northern Calcareous Prairie","West Gulf Coastal Plain Southern Calcareous Prairie","Acadian-Appalachian Subalpine Woodland and Heath-Krummholz","Atlantic and Gulf Coastal Plain Interdunal Wetland","Atlantic Coastal Plain Southern Dune and Maritime Grassland","Central and Upper Texas Coast Dune and Coastal Grassland","East Gulf Coastal Plain Dune and Coastal Grassland","Great Lakes Dune","Northern Atlantic Coastal Plain Dune and Swale","Northern Atlantic Coastal Plain Heathland and Grassland","South Texas Dune and Coastal Grassland","South Texas Sand Sheet Grassland","Southwest Florida Dune and Coastal Grassland","North Pacific Coastal Cliff and Bluff","North Pacific Maritime Coastal Sand Dune and Strand","Northern California Coastal Scrub","Mediterranean California Coastal Bluff","Mediterranean California Northern Coastal Dune","Mediterranean California Southern Coastal Dune","Atlantic Coastal Plain Northern Sandy Beach","Atlantic Coastal Plain Sea Island Beach","Atlantic Coastal Plain Southern Beach","Florida Panhandle Beach Vegetation","Louisiana Beach","Northern Atlantic Coastal Plain Sandy Beach","Texas Coastal Bend Beach","Upper Texas Coast Beach","0","Mediterranean California Serpentine Fen","Mediterranean California Subalpine-Montane Fen","North Pacific Bog and Fen","Rocky Mountain Subalpine-Montane Fen","Atlantic Coastal Plain Peatland Pocosin","Southern and Central Appalachian Bog and Fen","Atlantic Coastal Plain Central Fresh-Oligohaline Tidal Marsh","Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh","Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh","Florida Big Bend Fresh-Oligohaline Tidal Marsh","Atlantic Coastal Plain Depression Pondshore","Atlantic Coastal Plain Large Natural Lakeshore","Central Florida Herbaceous Pondshore","Central Florida Herbaceous Seep","East Gulf Coastal Plain Savanna and Wet Prairie","East Gulf Coastal Plain Depression Pondshore","Floridian Highlands Freshwater Marsh","Southern Coastal Plain Herbaceous Seepage Bog","Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier","Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier","Texas-Louisiana Coastal Prairie Slough","Central Interior and Appalachian Shrub-Herbaceous Wetland Systems","Great Lakes Coastal Marsh Systems","0","0","Laurentian-Acadian Shrub-Herbaceous Wetland Systems","0","Eastern Great Plains Wet Meadow, Prairie  and Marsh","Great Lakes Wet-Mesic Lakeplain Prairie","Great Plains Prairie Pothole","Western Great Plains Closed Depression Wetland","Western Great Plains Depressional Wetland Systems","Western Great Plains Open Freshwater Depression Wetland","Cumberland Riverscour","Inter-Mountain Basins Interdunal Swale Wetland","North Pacific Avalanche Chute Shrubland","North Pacific Intertidal Freshwater Wetland","Temperate Pacific Freshwater Emergent Marsh","Temperate Pacific Freshwater Mudflat","Columbia Plateau Vernal Pool","Northern California Claypan Vernal Pool","Northern Rocky Mountain Wooded Vernal Pool","Columbia Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe","Rocky Mountain Alpine-Montane Wet Meadow","Rocky Mountain Subalpine-Montane Riparian Shrubland","Temperate Pacific Montane Wet Meadow","Willamette Valley Wet Prairie","Chihuahuan-Sonoran Desert Bottomland and Swale Grassland","North American Arid West Emergent Marsh","North American Warm Desert Riparian Mesquite Bosque","Western Great Plains Saline Depression Wetland","Acadian Salt Marsh and Estuary Systems","Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh","Atlantic Coastal Plain Embayed Region Tidal Salt and Brackish Marsh","Atlantic Coastal Plain Indian River Lagoon Tidal Marsh","Atlantic Coastal Plain Northern Tidal Salt Marsh","Florida Big Bend Salt-Brackish Tidal Marsh","Gulf and Atlantic Coastal Plain Tidal Marsh Systems","Mississippi Sound Salt and Brackish Tidal Marsh","Texas Saline Coastal Prairie","Temperate Pacific Tidal Salt and Brackish Marsh","Inter-Mountain Basins Alkaline Closed Depression","Inter-Mountain Basins Greasewood Flat","Inter-Mountain Basins Playa","North American Warm Desert Playa","Apacherian-Chihuahuan Mesquite Upland Scrub","Apacherian-Chihuahuan Semi-Desert Grassland and Steppe","Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub","Chihuahuan Gypsophilous Grassland and Steppe","Chihuahuan Loamy Plains Desert Grassland","Chihuahuan Mixed Desert and Thorn Scrub","Chihuahuan Sandy Plains Semi-Desert Grassland","Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub","Chihuahuan Succulent Desert Scrub","Madrean Juniper Savanna","Mojave Mid-Elevation Mixed Desert Scrub","North American Warm Desert Active and Stabilized Dune","Sonora-Mojave Creosotebush-White Bursage Desert Scrub","Sonoran Mid-Elevation Desert Scrub","Sonoran Paloverde-Mixed Cacti Desert Scrub","Chihuahuan Mixed Salt Desert Scrub","Sonora-Mojave Mixed Salt Desert Scrub","North American Warm Desert Wash","South Texas Lomas","Tamaulipan Calcareous Thornscrub","Tamaulipan Clay Grassland","Tamaulipan Mesquite Upland Scrub","Tamaulipan Mixed Deciduous Thornscrub","Tamaulipan Savanna Grassland","Inter-Mountain Basins Mat Saltbush Shrubland","Inter-Mountain Basins Mixed Salt Desert Scrub","Inter-Mountain Basins Wash","Columbia Plateau Steppe and Grassland","Great Basin Xeric Mixed Sagebrush Shrubland","Inter-Mountain Basins Big Sagebrush Shrubland","Inter-Mountain Basins Big Sagebrush Steppe","Inter-Mountain Basins Montane Sagebrush Steppe","Colorado Plateau Mixed Low Sagebrush Shrubland","Columbia Plateau Low Sagebrush Steppe","Columbia Plateau Scabland Shrubland","Wyoming Basins Dwarf Sagebrush Shrubland and Steppe","Colorado Plateau Blackbrush-Mormon-tea Shrubland","Inter-Mountain Basins Semi-Desert Grassland","Inter-Mountain Basins Semi-Desert Shrub Steppe","Southern Colorado Plateau Sand Shrubland","Acadian-Appalachian Alpine Tundra","Rocky Mountain Alpine Dwarf-Shrubland","Rocky Mountain Alpine Fell-Field","Rocky Mountain Alpine Turf","Mediterranean California Alpine Dry Tundra","Mediterranean California Alpine Fell-Field","North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow","Rocky Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit","Temperate Pacific Intertidal Mudflat","Mediterranean California Eelgrass Bed","North Pacific Maritime Eelgrass Bed","South-Central Interior Large Floodplain - Herbaceous Modifier","East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous Modifier","Temperate Pacific Freshwater Aquatic Bed","Central California Coast Ranges Cliff and Canyon","Mediterranean California Serpentine Barrens","Southern California Coast Ranges Cliff and Canyon","Central Interior Acidic Cliff and Talus","Central Interior Calcareous Cliff and Talus","East Gulf Coastal Plain Dry Chalk Bluff","North-Central Appalachian Acidic Cliff and Talus","North-Central Appalachian Circumneutral Cliff and Talus","Southern Appalachian Montane Cliff","Southern Interior Acid Cliff","Southern Interior Calcareous Cliff","Southern Piedmont Cliff","Southern Appalachian Granitic Dome","Southern Appalachian Rocky Summit","Southern Piedmont Granite Flatrock","Rocky Mountain Cliff, Canyon and Massive Bedrock","Klamath-Siskiyou Cliff and Outcrop","North Pacific Montane Massive Bedrock, Cliff and Talus","North Pacific Serpentine Barren","North Pacific Active Volcanic Rock and Cinder Land","Sierra Nevada Cliff and Canyon","Western Great Plains Badland","Southwestern Great Plains Canyon","Western Great Plains Cliff and Outcrop","North American Warm Desert Badland","North American Warm Desert Bedrock Cliff and Outcrop","North American Warm Desert Pavement","North American Warm Desert Volcanic Rockland","Colorado Plateau Mixed Bedrock Canyon and Tableland","Columbia Plateau Ash and Tuff Badland","Geysers and Hot Springs","Inter-Mountain Basins Active and Stabilized Dune","Inter-Mountain Basins Cliff and Canyon","Inter-Mountain Basins Shale Badland","Inter-Mountain Basins Volcanic Rock and Cinder Land","Rocky Mountain Alpine Bedrock and Scree","Mediterranean California Alpine Bedrock and Scree","North Pacific Alpine and Subalpine Bedrock and Scree","Unconsolidated Shore","Undifferentiated Barren Land","North American Alpine Ice Field","Orchards Vineyards and Other High Structure Agriculture","Cultivated Cropland","Pasture/Hay","Introduced Upland Vegetation - Annual Grassland","Introduced Upland Vegetation - Perennial Grassland and Forbland","Modified/Managed Southern Tall Grassland","Introduced Upland Vegetation - Shrub","Introduced Riparian and Wetland Vegetation","Introduced Upland Vegetation - Treed","0","Disturbed, Non-specific","Recently Logged Areas","Harvested Forest - Grass/Forb Regeneration","Harvested Forest-Shrub Regeneration","Harvested Forest - Northwestern Conifer Regeneration","Recently Burned","Recently burned grassland","Recently burned shrubland","Recently burned forest","Disturbed/Successional - Grass/Forb Regeneration","Disturbed/Successional - Shrub Regeneration","Disturbed/Successional - Recently Chained Pinyon-Juniper","Open Water (Aquaculture)","Open Water (Brackish/Salt)","Open Water (Fresh)","Quarries, Mines, Gravel Pits and Oil Wells","Developed, Open Space","Developed, Low Intensity","Developed, Medium Intensity","Developed, High Intensity"]}]},"description":"The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States.  These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"GeoTIFF data","file:values":[{"values":[0],"summary":"0"},{"values":[1],"summary":"South Florida Bayhead Swamp"},{"values":[2],"summary":"South Florida Cypress Dome"},{"values":[3],"summary":"South Florida Dwarf Cypress Savanna"},{"values":[4],"summary":"South Florida Mangrove Swamp"},{"values":[5],"summary":"South Florida Hardwood Hammock"},{"values":[6],"summary":"Southeast Florida Coastal Strand and Maritime Hammock"},{"values":[7],"summary":"Southwest Florida Coastal Strand and Maritime Hammock"},{"values":[8],"summary":"South Florida Pine Rockland"},{"values":[9],"summary":"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory"},{"values":[10],"summary":"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory"},{"values":[11],"summary":"Atlantic Coastal Plain Upland Longleaf Pine Woodland"},{"values":[12],"summary":"Atlantic Coastal Plain Xeric River Dune"},{"values":[13],"summary":"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier"},{"values":[14],"summary":"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier"},{"values":[15],"summary":"Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier"},{"values":[16],"summary":"Florida Longleaf Pine Sandhill- Open Understory Modifier"},{"values":[17],"summary":"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland"},{"values":[18],"summary":"Atlantic Coastal Plain Central Maritime Forest"},{"values":[19],"summary":"Atlantic Coastal Plain Southern Maritime Forest"},{"values":[20],"summary":"Central and South Texas Coastal Fringe Forest and Woodland"},{"values":[21],"summary":"East Gulf Coastal Plain Limestone Forest"},{"values":[22],"summary":"East Gulf Coastal Plain Maritime Forest"},{"values":[23],"summary":"East Gulf Coastal Plain Southern Loess Bluff Forest"},{"values":[24],"summary":"East Gulf Coastal Plain Southern Mesic Slope Forest"},{"values":[25],"summary":"Mississippi Delta Maritime Forest"},{"values":[26],"summary":"Southern Coastal Plain Dry Upland Hardwood Forest"},{"values":[27],"summary":"Southern Coastal Plain Oak Dome and Hammock"},{"values":[28],"summary":"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland"},{"values":[29],"summary":"West Gulf Coastal Plain Mesic Hardwood Forest"},{"values":[30],"summary":"East-Central Texas Plains Pine Forest and Woodland"},{"values":[31],"summary":"West Gulf Coastal Plain Pine-Hardwood Forest"},{"values":[32],"summary":"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland"},{"values":[33],"summary":"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier"},{"values":[34],"summary":"Deciduous Plantations"},{"values":[35],"summary":"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier"},{"values":[36],"summary":"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier"},{"values":[37],"summary":"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier"},{"values":[38],"summary":"Evergreen Plantation or Managed Pine"},{"values":[39],"summary":"California Central Valley Mixed Oak Savanna"},{"values":[40],"summary":"California Coastal Closed-Cone Conifer Forest and Woodland"},{"values":[41],"summary":"California Coastal Live Oak Woodland and Savanna"},{"values":[42],"summary":"California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna"},{"values":[43],"summary":"Central and Southern California Mixed Evergreen Woodland"},{"values":[44],"summary":"Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland"},{"values":[45],"summary":"Southern California Oak Woodland and Savanna"},{"values":[46],"summary":"Madrean Encinal"},{"values":[47],"summary":"Madrean Pinyon-Juniper Woodland"},{"values":[48],"summary":"Madrean Pine-Oak Forest and Woodland"},{"values":[49],"summary":"Madrean Upper Montane Conifer-Oak Forest and Woodland"},{"values":[50],"summary":"Edwards Plateau Dry-Mesic Slope Forest and Woodland"},{"values":[51],"summary":"Edwards Plateau Limestone Savanna and Woodland"},{"values":[52],"summary":"Edwards Plateau Mesic Canyon"},{"values":[53],"summary":"Llano Uplift Acidic Forest, Woodland and Glade"},{"values":[54],"summary":"East Cascades Oak-Ponderosa Pine Forest and Woodland"},{"values":[55],"summary":"Mediterranean California Mixed Evergreen Forest"},{"values":[56],"summary":"Mediterranean California Mixed Oak Woodland"},{"values":[57],"summary":"North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland"},{"values":[58],"summary":"North Pacific Oak Woodland"},{"values":[59],"summary":"Edwards Plateau Limestone Shrubland"},{"values":[60],"summary":"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood"},{"values":[61],"summary":"Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier"},{"values":[62],"summary":"Central and Southern Appalachian Montane Oak Forest"},{"values":[63],"summary":"Central and Southern Appalachian Northern Hardwood Forest"},{"values":[64],"summary":"Central Appalachian Oak and Pine Forest"},{"values":[65],"summary":"Crosstimbers Oak Forest and Woodland"},{"values":[66],"summary":"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier"},{"values":[67],"summary":"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest"},{"values":[68],"summary":"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier"},{"values":[69],"summary":"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier"},{"values":[70],"summary":"East-Central Texas Plains Post Oak Savanna and Woodland"},{"values":[71],"summary":"Lower Mississippi River Dune Woodland and Forest"},{"values":[72],"summary":"Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest"},{"values":[73],"summary":"North-Central Interior Dry Oak Forest and Woodland"},{"values":[74],"summary":"North-Central Interior Dry-Mesic Oak Forest and Woodland"},{"values":[75],"summary":"Northeastern Interior Dry Oak Forest - Mixed Modifier"},{"values":[76],"summary":"Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier"},{"values":[77],"summary":"Northeastern Interior Dry Oak Forest-Hardwood Modifier"},{"values":[78],"summary":"Northeastern Interior Dry-Mesic Oak Forest"},{"values":[79],"summary":"Northern Atlantic Coastal Plain Dry Hardwood Forest"},{"values":[80],"summary":"Crowley's Ridge Sand Forest"},{"values":[81],"summary":"Ouachita Montane Oak Forest"},{"values":[82],"summary":"Ozark-Ouachita Dry Oak Woodland"},{"values":[83],"summary":"Ozark-Ouachita Dry-Mesic Oak Forest"},{"values":[84],"summary":"Southern and Central Appalachian Oak Forest"},{"values":[85],"summary":"Southern and Central Appalachian Oak Forest - Xeric"},{"values":[86],"summary":"Southern Interior Low Plateau Dry-Mesic Oak Forest"},{"values":[87],"summary":"Southern Ridge and Valley Dry Calcareous Forest"},{"values":[88],"summary":"Southern Ridge and Valley Dry Calcareous Forest - Pine modifier"},{"values":[89],"summary":"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier"},{"values":[90],"summary":"Managed Tree Plantation"},{"values":[91],"summary":"Ruderal forest"},{"values":[92],"summary":"Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier"},{"values":[93],"summary":"Acadian Low-Elevation Spruce-Fir-Hardwood Forest"},{"values":[94],"summary":"Acadian-Appalachian Montane Spruce-Fir Forest"},{"values":[95],"summary":"Appalachian Hemlock-Hardwood Forest"},{"values":[96],"summary":"Central and Southern Appalachian Spruce-Fir Forest"},{"values":[97],"summary":"0"},{"values":[98],"summary":"Laurentian-Acadian Northern Hardwoods Forest"},{"values":[99],"summary":"Laurentian-Acadian Northern Pine-(Oak) Forest"},{"values":[100],"summary":"Laurentian-Acadian Pine-Hemlock-Hardwood Forest"},{"values":[101],"summary":"Paleozoic Plateau Bluff and Talus"},{"values":[102],"summary":"Southern Appalachian Northern Hardwood Forest"},{"values":[103],"summary":"Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest"},{"values":[104],"summary":"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood"},{"values":[105],"summary":"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier"},{"values":[106],"summary":"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier"},{"values":[107],"summary":"Ozark-Ouachita Shortleaf Pine-Bluestem Woodland"},{"values":[108],"summary":"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland"},{"values":[109],"summary":"Southeastern Interior Longleaf Pine Woodland"},{"values":[110],"summary":"Southern Appalachian Low Mountain Pine Forest"},{"values":[111],"summary":"Southern Piedmont Dry Oak-(Pine) Forest"},{"values":[112],"summary":"Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier"},{"values":[113],"summary":"Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier"},{"values":[114],"summary":"Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier"},{"values":[115],"summary":"Eastern Great Plains Tallgrass Aspen Parkland"},{"values":[116],"summary":"Northwestern Great Plains Aspen Forest and Parkland"},{"values":[117],"summary":"Northwestern Great Plains Shrubland"},{"values":[118],"summary":"Western Great Plains Dry Bur Oak Forest and Woodland"},{"values":[119],"summary":"Western Great Plains Wooded Draw and Ravine"},{"values":[120],"summary":"Southern Atlantic Coastal Plain Mesic Hardwood Forest"},{"values":[121],"summary":"East Gulf Coastal Plain Northern Loess Bluff Forest"},{"values":[122],"summary":"East Gulf Coastal Plain Northern Mesic Hardwood Forest"},{"values":[123],"summary":"North-Central Interior Beech-Maple Forest"},{"values":[124],"summary":"North-Central Interior Maple-Basswood Forest"},{"values":[125],"summary":"Ozark-Ouachita Mesic Hardwood Forest"},{"values":[126],"summary":"South-Central Interior Mesophytic Forest"},{"values":[127],"summary":"Southern and Central Appalachian Cove Forest"},{"values":[128],"summary":"Crowley's Ridge Mesic Loess Slope Forest"},{"values":[129],"summary":"Southern Piedmont Mesic Forest"},{"values":[130],"summary":"Appalachian Shale Barrens"},{"values":[131],"summary":"Atlantic Coastal Plain Northern Maritime Forest"},{"values":[132],"summary":"Laurentian Pine-Oak Barrens"},{"values":[133],"summary":"Northeastern Interior Pine Barrens"},{"values":[134],"summary":"Northern Atlantic Coastal Plain Pitch Pine Barrens"},{"values":[135],"summary":"Southern Appalachian Montane Pine Forest and Woodland"},{"values":[136],"summary":"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland"},{"values":[137],"summary":"Middle Rocky Mountain Montane Douglas-fir Forest and Woodland"},{"values":[138],"summary":"Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest"},{"values":[139],"summary":"Northern Rocky Mountain Foothill Conifer Wooded Steppe"},{"values":[140],"summary":"Northern Rocky Mountain Mesic Montane Mixed Conifer Forest"},{"values":[141],"summary":"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna"},{"values":[142],"summary":"Northern Rocky Mountain Western Larch Savanna"},{"values":[143],"summary":"Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna"},{"values":[144],"summary":"Rocky Mountain Foothill Limber Pine-Juniper Woodland"},{"values":[145],"summary":"Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland"},{"values":[146],"summary":"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland"},{"values":[147],"summary":"Northern Rocky Mountain Subalpine Woodland and Parkland"},{"values":[148],"summary":"Rocky Mountain Aspen Forest and Woodland"},{"values":[149],"summary":"Rocky Mountain Lodgepole Pine Forest"},{"values":[150],"summary":"Rocky Mountain Poor-Site Lodgepole Pine Forest"},{"values":[151],"summary":"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland"},{"values":[152],"summary":"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland"},{"values":[153],"summary":"Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland"},{"values":[154],"summary":"Rocky Mountain Bigtooth Maple Ravine Woodland"},{"values":[155],"summary":"Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland"},{"values":[156],"summary":"Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland"},{"values":[157],"summary":"Southern Rocky Mountain Ponderosa Pine Savanna"},{"values":[158],"summary":"Southern Rocky Mountain Ponderosa Pine Woodland"},{"values":[159],"summary":"California Montane Jeffrey Pine-(Ponderosa Pine) Woodland"},{"values":[160],"summary":"Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland"},{"values":[161],"summary":"Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland"},{"values":[162],"summary":"Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland"},{"values":[163],"summary":"Mediterranean California Mesic Mixed Conifer Forest and Woodland"},{"values":[164],"summary":"Sierran-Intermontane Desert Western White Pine-White Fir Woodland"},{"values":[165],"summary":"California Coastal Redwood Forest"},{"values":[166],"summary":"North Pacific Broadleaf Landslide Forest and Shrubland"},{"values":[167],"summary":"North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest"},{"values":[168],"summary":"North Pacific Hypermaritime Sitka Spruce Forest"},{"values":[169],"summary":"North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest"},{"values":[170],"summary":"North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland"},{"values":[171],"summary":"North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest"},{"values":[172],"summary":"North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest"},{"values":[173],"summary":"North Pacific Mesic Western Hemlock-Silver Fir Forest"},{"values":[174],"summary":"North Pacific Wooded Volcanic Flowage"},{"values":[175],"summary":"Mediterranean California Red Fir Forest"},{"values":[176],"summary":"Mediterranean California Subalpine Woodland"},{"values":[177],"summary":"North Pacific Maritime Mesic Subalpine Parkland"},{"values":[178],"summary":"North Pacific Mountain Hemlock Forest"},{"values":[179],"summary":"Northern California Mesic Subalpine Woodland"},{"values":[180],"summary":"Northern Pacific Mesic Subalpine Woodland"},{"values":[181],"summary":"Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland"},{"values":[182],"summary":"Columbia Plateau Western Juniper Woodland and Savanna"},{"values":[183],"summary":"Great Basin Pinyon-Juniper Woodland"},{"values":[184],"summary":"Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland"},{"values":[185],"summary":"Inter-Mountain Basins Juniper Savanna"},{"values":[186],"summary":"Colorado Plateau Pinyon-Juniper Shrubland"},{"values":[187],"summary":"Colorado Plateau Pinyon-Juniper Woodland"},{"values":[188],"summary":"Southern Rocky Mountain Juniper Woodland and Savanna"},{"values":[189],"summary":"Southern Rocky Mountain Pinyon-Juniper Woodland"},{"values":[190],"summary":"Northwestern Great Plains Floodplain"},{"values":[191],"summary":"Northwestern Great Plains Riparian"},{"values":[192],"summary":"Western Great Plains Floodplain"},{"values":[193],"summary":"Western Great Plains Floodplain Systems"},{"values":[194],"summary":"Western Great Plains Riparian Woodland and Shrubland"},{"values":[195],"summary":"Central Appalachian Floodplain - Forest Modifier"},{"values":[196],"summary":"Central Appalachian Riparian - Forest Modifier"},{"values":[197],"summary":"Central Interior and Appalachian Floodplain Systems"},{"values":[198],"summary":"Central Interior and Appalachian Riparian Systems"},{"values":[199],"summary":"Laurentian-Acadian Floodplain Systems"},{"values":[200],"summary":"Ozark-Ouachita Riparian"},{"values":[201],"summary":"South-Central Interior Large Floodplain"},{"values":[202],"summary":"South-Central Interior Large Floodplain - Forest Modifier"},{"values":[203],"summary":"South-Central Interior Small Stream and Riparian"},{"values":[204],"summary":"North-Central Interior and Appalachian Rich Swamp"},{"values":[205],"summary":"0"},{"values":[206],"summary":"0"},{"values":[207],"summary":"Laurentian-Acadian Swamp Systems"},{"values":[208],"summary":"North-Central Interior Wet Flatwoods"},{"values":[209],"summary":"0"},{"values":[210],"summary":"South-Central Interior / Upper Coastal Plain Wet Flatwoods"},{"values":[211],"summary":"0"},{"values":[212],"summary":"Southern Piedmont/Ridge and Valley Upland Depression Swamp"},{"values":[213],"summary":"Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier"},{"values":[214],"summary":"Atlantic Coastal Plain Brownwater Stream Floodplain Forest"},{"values":[215],"summary":"Atlantic Coastal Plain Northern Tidal Wooded Swamp"},{"values":[216],"summary":"Atlantic Coastal Plain Small Blackwater River Floodplain Forest"},{"values":[217],"summary":"Atlantic Coastal Plain Small Brownwater River Floodplain Forest"},{"values":[218],"summary":"Atlantic Coastal Plain Southern Tidal Wooded Swamp"},{"values":[219],"summary":"East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier"},{"values":[220],"summary":"East Gulf Coastal Plain Small Stream and River Floodplain Forest"},{"values":[221],"summary":"East Gulf Coastal Plain Tidal Wooded Swamp"},{"values":[222],"summary":"0"},{"values":[223],"summary":"Southeastern Great Plains Riparian Forest"},{"values":[224],"summary":"Southeastern Great Plains Floodplain Forest"},{"values":[225],"summary":"Mississippi River Bottomland Depression"},{"values":[226],"summary":"Mississippi River Floodplain and Riparian Forest"},{"values":[227],"summary":"Mississippi River Low Floodplain (Bottomland) Forest"},{"values":[228],"summary":"Mississippi River Riparian Forest"},{"values":[229],"summary":"Red River Large Floodplain Forest"},{"values":[230],"summary":"Southern Coastal Plain Blackwater River Floodplain Forest"},{"values":[231],"summary":"Southern Piedmont Large Floodplain Forest - Forest Modifier"},{"values":[232],"summary":"Southern Piedmont Small Floodplain and Riparian Forest"},{"values":[233],"summary":"West Gulf Coastal Plain Large River Floodplain Forest"},{"values":[234],"summary":"West Gulf Coastal Plain Near-Coast Large River Swamp"},{"values":[235],"summary":"West Gulf Coastal Plain Small Stream and River Forest"},{"values":[236],"summary":"Atlantic Coastal Plain Streamhead Seepage Swamp -  Pocosin -  and Baygall"},{"values":[237],"summary":"Gulf and Atlantic Coastal Plain Swamp Systems"},{"values":[238],"summary":"Southern Coastal Plain Hydric Hammock"},{"values":[239],"summary":"Southern Coastal Plain Seepage Swamp and Baygall"},{"values":[240],"summary":"West Gulf Coastal Plain Seepage Swamp and Baygall"},{"values":[241],"summary":"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest  - Taxodium/Nyssa Modifier"},{"values":[242],"summary":"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier"},{"values":[243],"summary":"East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods"},{"values":[244],"summary":"Lower Mississippi River Bottomland Depressions - Forest Modifier"},{"values":[245],"summary":"Lower Mississippi River Flatwoods"},{"values":[246],"summary":"Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest"},{"values":[247],"summary":"Southern Coastal Plain Nonriverine Basin Swamp"},{"values":[248],"summary":"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier"},{"values":[249],"summary":"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier"},{"values":[250],"summary":"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier"},{"values":[251],"summary":"West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods"},{"values":[252],"summary":"West Gulf Coastal Plain Pine-Hardwood Flatwoods"},{"values":[253],"summary":"Edwards Plateau Riparian"},{"values":[254],"summary":"Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland"},{"values":[255],"summary":"Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland"},{"values":[256],"summary":"Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods"},{"values":[257],"summary":"Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods"},{"values":[258],"summary":"Central Florida Pine Flatwoods"},{"values":[259],"summary":"East Gulf Coastal Plain Near-Coast Pine Flatwoods"},{"values":[260],"summary":"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier"},{"values":[261],"summary":"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier"},{"values":[262],"summary":"South Florida Pine Flatwoods"},{"values":[263],"summary":"Southern Coastal Plain Nonriverine Cypress Dome"},{"values":[264],"summary":"West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods"},{"values":[265],"summary":"Columbia Basin Foothill Riparian Woodland and Shrubland"},{"values":[266],"summary":"Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland"},{"values":[267],"summary":"0"},{"values":[268],"summary":"Northern Rocky Mountain Conifer Swamp"},{"values":[269],"summary":"Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland"},{"values":[270],"summary":"Rocky Mountain Lower Montane Riparian Woodland and Shrubland"},{"values":[271],"summary":"Rocky Mountain Montane Riparian Systems"},{"values":[272],"summary":"Rocky Mountain Subalpine-Montane Riparian Woodland"},{"values":[273],"summary":"North Pacific Hardwood-Conifer Swamp"},{"values":[274],"summary":"North Pacific Lowland Riparian Forest and Shrubland"},{"values":[275],"summary":"North Pacific Montane Riparian Woodland and Shrubland"},{"values":[276],"summary":"North Pacific Shrub Swamp"},{"values":[277],"summary":"California Central Valley Riparian Woodland and Shrubland"},{"values":[278],"summary":"Mediterranean California Foothill and Lower Montane Riparian Woodland"},{"values":[279],"summary":"Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep"},{"values":[280],"summary":"North American Warm Desert Lower Montane Riparian Woodland and Shrubland"},{"values":[281],"summary":"North American Warm Desert Riparian Systems"},{"values":[282],"summary":"North American Warm Desert Riparian Woodland and Shrubland"},{"values":[283],"summary":"Tamaulipan Floodplain"},{"values":[284],"summary":"Tamaulipan Riparian Systems"},{"values":[285],"summary":"Boreal Aspen-Birch Forest"},{"values":[286],"summary":"Boreal Jack Pine-Black Spruce Forest"},{"values":[287],"summary":"Boreal White Spruce-Fir-Hardwood Forest"},{"values":[288],"summary":"Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen"},{"values":[289],"summary":"Eastern Boreal Floodplain"},{"values":[290],"summary":"South Florida Shell Hash Beach"},{"values":[291],"summary":"Southeast Florida Beach"},{"values":[292],"summary":"Southwest Florida Beach"},{"values":[293],"summary":"South Florida Everglades Sawgrass Marsh"},{"values":[294],"summary":"South Florida Freshwater Slough and Gator Hole"},{"values":[295],"summary":"South Florida Wet Marl Prairie"},{"values":[296],"summary":"California Maritime Chaparral"},{"values":[297],"summary":"California Mesic Chaparral"},{"values":[298],"summary":"California Xeric Serpentine Chaparral"},{"values":[299],"summary":"Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral"},{"values":[300],"summary":"Mediterranean California Mesic Serpentine Woodland and Chaparral"},{"values":[301],"summary":"Northern and Central California Dry-Mesic Chaparral"},{"values":[302],"summary":"Southern California Dry-Mesic Chaparral"},{"values":[303],"summary":"Southern California Coastal Scrub"},{"values":[304],"summary":"California Central Valley and Southern Coastal Grassland"},{"values":[305],"summary":"California Mesic Serpentine Grassland"},{"values":[306],"summary":"Columbia Basin Foothill and Canyon Dry Grassland"},{"values":[307],"summary":"Columbia Basin Palouse Prairie"},{"values":[308],"summary":"North Pacific Alpine and Subalpine Dry Grassland"},{"values":[309],"summary":"North Pacific Montane Grassland"},{"values":[310],"summary":"North Pacific Montane Shrubland"},{"values":[311],"summary":"Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland"},{"values":[312],"summary":"Northern Rocky Mountain Montane-Foothill Deciduous Shrubland"},{"values":[313],"summary":"Northern Rocky Mountain Subalpine Deciduous Shrubland"},{"values":[314],"summary":"Northern Rocky Mountain Subalpine-Upper Montane Grassland"},{"values":[315],"summary":"Southern Rocky Mountain Montane-Subalpine Grassland"},{"values":[316],"summary":"Rocky Mountain Gambel Oak-Mixed Montane Shrubland"},{"values":[317],"summary":"Rocky Mountain Lower Montane-Foothill Shrubland"},{"values":[318],"summary":"California Northern Coastal Grassland"},{"values":[319],"summary":"North Pacific Herbaceous Bald and Bluff"},{"values":[320],"summary":"North Pacific Hypermaritime Shrub and Herbaceous Headland"},{"values":[321],"summary":"Willamette Valley Upland Prairie and Savanna"},{"values":[322],"summary":"Mediterranean California Subalpine Meadow"},{"values":[323],"summary":"Rocky Mountain Subalpine-Montane Mesic Meadow"},{"values":[324],"summary":"Central Mixedgrass Prairie"},{"values":[325],"summary":"Northwestern Great Plains Mixedgrass Prairie"},{"values":[326],"summary":"Western Great Plains Foothill and Piedmont Grassland"},{"values":[327],"summary":"Western Great Plains Tallgrass Prairie"},{"values":[328],"summary":"Western Great Plains Sand Prairie"},{"values":[329],"summary":"Western Great Plains Sandhill Steppe"},{"values":[330],"summary":"Western Great Plains Mesquite Woodland and Shrubland"},{"values":[331],"summary":"Western Great Plains Shortgrass Prairie"},{"values":[332],"summary":"Arkansas Valley Prairie and Woodland"},{"values":[333],"summary":"Central Tallgrass Prairie"},{"values":[334],"summary":"North-Central Interior Oak Savanna"},{"values":[335],"summary":"North-Central Interior Sand and Gravel Tallgrass Prairie"},{"values":[336],"summary":"North-Central Oak Barrens"},{"values":[337],"summary":"Northern Tallgrass Prairie"},{"values":[338],"summary":"Southeastern Great Plains Tallgrass Prairie"},{"values":[339],"summary":"Texas Blackland Tallgrass Prairie"},{"values":[340],"summary":"Texas-Louisiana Coastal Prairie"},{"values":[341],"summary":"Central Appalachian Pine-Oak Rocky Woodland"},{"values":[342],"summary":"Southern Appalachian Grass and Shrub Bald"},{"values":[343],"summary":"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier"},{"values":[344],"summary":"Southern Appalachian Grass and Shrub Bald - Shrub Modifier"},{"values":[345],"summary":"Central Appalachian Alkaline Glade and Woodland"},{"values":[346],"summary":"Central Interior Highlands Calcareous Glade and Barrens"},{"values":[347],"summary":"Central Interior Highlands Dry Acidic Glade and Barrens"},{"values":[348],"summary":"Cumberland Sandstone Glade and Barrens"},{"values":[349],"summary":"Great Lakes Alvar"},{"values":[350],"summary":"Nashville Basin Limestone Glade"},{"values":[351],"summary":"Southern Ridge and Valley / Cumberland Dry Calcareous Forest"},{"values":[352],"summary":"Southern Piedmont Glade and Barrens"},{"values":[353],"summary":"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier"},{"values":[354],"summary":"East Gulf Coastal Plain Jackson Prairie and Woodland"},{"values":[355],"summary":"Eastern Highland Rim Prairie and Barrens - Dry Modifier"},{"values":[356],"summary":"Coahuilan Chaparral"},{"values":[357],"summary":"Madrean Oriental Chaparral"},{"values":[358],"summary":"Mogollon Chaparral"},{"values":[359],"summary":"Sonora-Mojave Semi-Desert Chaparral"},{"values":[360],"summary":"California Montane Woodland and Chaparral"},{"values":[361],"summary":"Great Basin Semi-Desert Chaparral"},{"values":[362],"summary":"Florida Dry Prairie"},{"values":[363],"summary":"Florida Peninsula Inland Scrub"},{"values":[364],"summary":"West Gulf Coastal Plain Catahoula Barrens"},{"values":[365],"summary":"West Gulf Coastal Plain Nepheline Syenite Glade"},{"values":[366],"summary":"East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier"},{"values":[367],"summary":"West Gulf Coastal Plain Northern Calcareous Prairie"},{"values":[368],"summary":"West Gulf Coastal Plain Southern Calcareous Prairie"},{"values":[369],"summary":"Acadian-Appalachian Subalpine Woodland and Heath-Krummholz"},{"values":[370],"summary":"Atlantic and Gulf Coastal Plain Interdunal Wetland"},{"values":[371],"summary":"Atlantic Coastal Plain Southern Dune and Maritime Grassland"},{"values":[372],"summary":"Central and Upper Texas Coast Dune and Coastal Grassland"},{"values":[373],"summary":"East Gulf Coastal Plain Dune and Coastal Grassland"},{"values":[374],"summary":"Great Lakes Dune"},{"values":[375],"summary":"Northern Atlantic Coastal Plain Dune and Swale"},{"values":[376],"summary":"Northern Atlantic Coastal Plain Heathland and Grassland"},{"values":[377],"summary":"South Texas Dune and Coastal Grassland"},{"values":[378],"summary":"South Texas Sand Sheet Grassland"},{"values":[379],"summary":"Southwest Florida Dune and Coastal Grassland"},{"values":[380],"summary":"North Pacific Coastal Cliff and Bluff"},{"values":[381],"summary":"North Pacific Maritime Coastal Sand Dune and Strand"},{"values":[382],"summary":"Northern California Coastal Scrub"},{"values":[383],"summary":"Mediterranean California Coastal Bluff"},{"values":[384],"summary":"Mediterranean California Northern Coastal Dune"},{"values":[385],"summary":"Mediterranean California Southern Coastal Dune"},{"values":[386],"summary":"Atlantic Coastal Plain Northern Sandy Beach"},{"values":[387],"summary":"Atlantic Coastal Plain Sea Island Beach"},{"values":[388],"summary":"Atlantic Coastal Plain Southern Beach"},{"values":[389],"summary":"Florida Panhandle Beach Vegetation"},{"values":[390],"summary":"Louisiana Beach"},{"values":[391],"summary":"Northern Atlantic Coastal Plain Sandy Beach"},{"values":[392],"summary":"Texas Coastal Bend Beach"},{"values":[393],"summary":"Upper Texas Coast Beach"},{"values":[394],"summary":"0"},{"values":[395],"summary":"Mediterranean California Serpentine Fen"},{"values":[396],"summary":"Mediterranean California Subalpine-Montane Fen"},{"values":[397],"summary":"North Pacific Bog and Fen"},{"values":[398],"summary":"Rocky Mountain Subalpine-Montane Fen"},{"values":[399],"summary":"Atlantic Coastal Plain Peatland Pocosin"},{"values":[400],"summary":"Southern and Central Appalachian Bog and Fen"},{"values":[401],"summary":"Atlantic Coastal Plain Central Fresh-Oligohaline Tidal 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Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Data","description":"a 'true-colour' representation of spatially accurate data with minimized haze, illumination, and topographic effects"},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"Thumbnail"}},"stac_version":"1.0.0","msft:group_id":"planet-nicfi","msft:container":"nicfi","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"planet","msft:short_description":"Planet's high-resolution, analysis-ready mosaics of the world's tropics","msft:region":"westeurope"},{"id":"gbif","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif"},{"rel":"license","href":"https://www.gbif.org/terms","type":"text/html","title":"GBIF Terms of Use"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/gbif","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Global Biodiversity Information Facility (GBIF)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gbif.png","type":"image/png","title":"Forest Inventory and Analysis"},"geoparquet-items":{"href":"abfs://items/gbif.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-04-13T00:00:00Z",null]]}},"license":"proprietary","keywords":["GBIF","Biodiversity","Species"],"providers":[{"url":"https://www.gbif.org/","name":"Global Biodiversity Information Facility","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below.  Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc.  Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"ai4edataeuwest"}}},"stac_version":"1.0.0","table:columns":[{"name":"gbifid","type":"int64","description":"GBIF's identifier for the occurrence"},{"name":"datasetkey","type":"byte_array","description":"GBIF's UUID for the [dataset](https://www.gbif.org/developer/registry#datasets) containing this occurrence"},{"name":"occurrenceid","type":"byte_array","description":"See [dwc:occurrenceID](https://dwc.tdwg.org/terms/#occurrenceID)."},{"name":"kingdom","type":"byte_array","description":"See [dwc:kingdom](https://dwc.tdwg.org/terms/#kingdom).  This field has been aligned with the [GBIF backbone taxonomy](https://doi.org/10.15468/39omei)."},{"name":"phylum","type":"byte_array","description":"See [dwc:phylum](https://dwc.tdwg.org/terms/#phylum).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"class","type":"byte_array","description":"See [dwc:class](https://dwc.tdwg.org/terms/#class).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"order","type":"byte_array","description":"See [dwc:order](https://dwc.tdwg.org/terms/#order).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"family","type":"byte_array","description":"See [dwc:family](https://dwc.tdwg.org/terms/#family).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"genus","type":"byte_array","description":"See [dwc:genus](https://dwc.tdwg.org/terms/#genus).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"species","type":"byte_array","description":"See [dwc:species](https://dwc.tdwg.org/terms/#species).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"infraspecificepithet","type":"byte_array","description":"See [dwc:infraspecificEpithet](https://dwc.tdwg.org/terms/#infraspecificEpithet).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"taxonrank","type":"byte_array","description":"See [dwc:taxonRank](https://dwc.tdwg.org/terms/#taxonRank).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"scientificname","type":"byte_array","description":"See [dwc:scientificName](https://dwc.tdwg.org/terms/#scientificName).  This field has been aligned with the GBIF backbone taxonomy."},{"name":"verbatimscientificname","type":"byte_array","description":"The scientific name as provided by the data publisher"},{"name":"verbatimscientificnameauthorship","type":"byte_array","description":"The scientific name authorship provided by the data publisher."},{"name":"countrycode","type":"byte_array","description":"See [dwc:countryCode](https://dwc.tdwg.org/terms/#countryCode).  GBIF's interpretation has set this to an ISO 3166-2 code."},{"name":"locality","type":"byte_array","description":"See [dwc:locality](https://dwc.tdwg.org/terms/#locality)."},{"name":"stateprovince","type":"byte_array","description":"See [dwc:stateProvince](https://dwc.tdwg.org/terms/#stateProvince)."},{"name":"occurrencestatus","type":"byte_array","description":"See [dwc:occurrenceStatus](https://dwc.tdwg.org/terms/#occurrenceStatus). Either the value `PRESENT` or `ABSENT`.  **Many users will wish to filter for `PRESENT` data.**"},{"name":"individualcount","type":"int32","description":"See [dwc:individualCount](https://dwc.tdwg.org/terms/#individualCount)."},{"name":"publishingorgkey","type":"byte_array","description":"GBIF's UUID for the [organization](https://www.gbif.org/developer/registry#organizations) publishing this occurrence."},{"name":"decimallatitude","type":"double","description":"See [dwc:decimalLatitude](https://dwc.tdwg.org/terms/#decimalLatitude).  GBIF's interpretation has normalized this to a WGS84 coordinate."},{"name":"decimallongitude","type":"double","description":"See [dwc:decimalLongitude](https://dwc.tdwg.org/terms/#decimalLongitude).  GBIF's interpretation has normalized this to a WGS84 coordinate."},{"name":"coordinateuncertaintyinmeters","type":"double","description":"See [dwc:coordinateUncertaintyInMeters](https://dwc.tdwg.org/terms/#coordinateUncertaintyInMeters)."},{"name":"coordinateprecision","type":"double","description":"See [dwc:coordinatePrecision](https://dwc.tdwg.org/terms/#coordinatePrecision)."},{"name":"elevation","type":"double","description":"See [dwc:elevation](https://dwc.tdwg.org/terms/#elevation).  If provided by the data publisher, GBIF's interpretation has normalized this value to metres."},{"name":"elevationaccuracy","type":"double","description":"See [dwc:elevationAccuracy](https://dwc.tdwg.org/terms/#elevationAccuracy).  If provided by the data publisher, GBIF's interpretation has normalized this value to metres."},{"name":"depth","type":"double","description":"See [dwc:depth](https://dwc.tdwg.org/terms/#depth).  If provided by the data publisher, GBIF's interpretation has normalized this value to metres."},{"name":"depthaccuracy","type":"double","description":"See [dwc:depthAccuracy](https://dwc.tdwg.org/terms/#depthAccuracy).  If provided by the data publisher, GBIF's interpretation has normalized this value to metres."},{"name":"eventdate","type":"byte_array","description":"See [dwc:eventDate](https://dwc.tdwg.org/terms/#eventDate).  GBIF's interpretation has normalized this value to an ISO 8601 date with a local time."},{"name":"day","type":"int32","description":"See [dwc:day](https://dwc.tdwg.org/terms/#day)."},{"name":"month","type":"int32","description":"See [dwc:month](https://dwc.tdwg.org/terms/#month)."},{"name":"year","type":"int32","description":"See [dwc:year](https://dwc.tdwg.org/terms/#year)."},{"name":"taxonkey","type":"int32","description":"The numeric identifier for the [taxon](https://www.gbif.org/developer/species#nameUsages) in GBIF's backbone taxonomy corresponding to `scientificname`."},{"name":"specieskey","type":"int32","description":"The numeric identifier for the taxon in GBIF's backbone taxonomy corresponding to `species`."},{"name":"basisofrecord","type":"byte_array","description":"See [dwc:basisOfRecord](https://dwc.tdwg.org/terms/#basisOfRecord).  One of `PRESERVED_SPECIMEN`, `FOSSIL_SPECIMEN`, `LIVING_SPECIMEN`, `OBSERVATION`, `HUMAN_OBSERVATION`, `MACHINE_OBSERVATION`, `MATERIAL_SAMPLE`, `LITERATURE`, `UNKNOWN`."},{"name":"institutioncode","type":"byte_array","description":"See [dwc:institutionCode](https://dwc.tdwg.org/terms/#institutionCode)."},{"name":"collectioncode","type":"byte_array","description":"See [dwc:collectionCode](https://dwc.tdwg.org/terms/#collectionCode)."},{"name":"catalognumber","type":"byte_array","description":"See [dwc:catalogNumber](https://dwc.tdwg.org/terms/#catalogNumber)."},{"name":"recordnumber","type":"byte_array","description":"See [dwc:recordNumber](https://dwc.tdwg.org/terms/#recordNumber)."},{"name":"identifiedby","type":"byte_array","description":"See [dwc:identifiedBy](https://dwc.tdwg.org/terms/#identifiedBy)."},{"name":"dateidentified","type":"byte_array","description":"See [dwc:dateIdentified](https://dwc.tdwg.org/terms/#dateIdentified). An ISO 8601 date."},{"name":"license","type":"byte_array","description":"See [dwc:license](https://dwc.tdwg.org/terms/#license). Either [`CC0_1_0`](https://creativecommons.org/publicdomain/zero/1.0/) or [`CC_BY_4_0`](https://creativecommons.org/licenses/by/4.0/).  `CC_BY_NC_4_0` records are not present in this snapshot."},{"name":"rightsholder","type":"byte_array","description":"See [dwc:rightsHolder](https://dwc.tdwg.org/terms/#rightsHolder)."},{"name":"recordedby","type":"byte_array","description":"See [dwc:recordedBy](https://dwc.tdwg.org/terms/#recordedBy)."},{"name":"typestatus","type":"byte_array","description":"See [dwc:typeStatus](https://dwc.tdwg.org/terms/#typeStatus)."},{"name":"establishmentmeans","type":"byte_array","description":"See [dwc:establishmentMeans](https://dwc.tdwg.org/terms/#establishmentMeans)."},{"name":"lastinterpreted","type":"byte_array","description":"The ISO 8601 date when the record was last processed by GBIF. Data are reprocessed for several reasons, including changes to the backbone taxonomy, so this date is not necessarily the date the occurrence record last changed."},{"name":"mediatype","type":"byte_array","description":"See [dwc:mediaType](https://dwc.tdwg.org/terms/#mediaType).  May contain `StillImage`, `MovingImage` or `Sound` (from [enumeration](http://api.gbif.org/v1/enumeration/basic/MediaType), detailing whether the occurrence has this media available."},{"name":"issue","type":"byte_array","description":"A list of [issues](https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/OccurrenceIssue.html) encountered by GBIF in processing this record. More details are available on these issues and flags in [this blog post](https://data-blog.gbif.org/post/issues-and-flags/)."}],"msft:container":"gbif","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Global biodiversity observation records, documenting over 1.6 billion species occurrences","msft:region":"westeurope"},{"id":"modis-17A3HGF-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A3HGF-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A3HGF-061"},{"rel":"help","href":"https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf","title":"MOD17 User Guide"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A3HGF","title":"MOD17A3HGF file specification"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A3HGF","title":"MYD17A3HGF file specification"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD17A3HGF.061","title":"LP DAAC - MOD17A3HGF"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD17A3HGF.061","title":"LP DAAC - MYD17A3HGF"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"LP DAAC - Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A3HGF-061/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/modis-17A3HGF-061","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MODIS Net Primary Production Yearly Gap-Filled","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A3HGF-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Net Primary Production Yearly Gap-Filled thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-17A3HGF-061.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"license":"proprietary","keywords":["NASA","MODIS","Satellite","Vegetation","Global","MOD17A3HGF","MYD17A3HGF"],"providers":[{"url":"https://lpdaac.usgs.gov/","name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["aqua","terra"],"instruments":["modis"]},"description":"The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.","item_assets":{"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"Gpp_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Gross Primary Productivity","raster:bands":[{"unit":"kg C/m^2","scale":0.0001,"data_type":"uint16","spatial_resolution":500}]},"Npp_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Net Primary Productivity","raster:bands":[{"unit":"kg C/m^2","scale":0.0001,"data_type":"int16","spatial_resolution":500}]},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"Npp_QC_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Quality control","raster:bands":[{"unit":"Percent","data_type":"uint8","spatial_resolution":500}]}},"stac_version":"1.0.0","msft:group_id":"modis","msft:container":"modis-061","stac_extensions":["https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.5067/MODIS/MOD17A3HGF.061","citation":"Running, S., &amp; Zhao, M. (2021). <i>MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A3HGF.061"},{"doi":"10.5067/MODIS/MYD17A3HGF.061","citation":"Running, S., &amp; Zhao, M. (2021). <i>MODIS/Aqua Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD17A3HGF.061"}],"msft:storage_account":"modiseuwest","msft:short_description":"MODIS Net Primary Production Yearly Gap-Filled","msft:region":"westeurope"},{"id":"modis-09A1-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-09A1-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-09A1-061"},{"rel":"help","href":"https://lpdaac.usgs.gov/documents/925/MOD09_User_Guide_V61.pdf","title":"MOD09 User Guide"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09A1","title":"MOD09A1 file specification"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09A1","title":"MYD09A1 file specification"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD09A1.061","title":"LP DAAC - MOD09A1"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD09A1.061","title":"LP DAAC - MYD09A1"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"LP DAAC - Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-09A1-061/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/modis-09A1-061","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MODIS Surface Reflectance 8-Day (500m)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-09A1-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Surface Reflectance 8-Day (500m) thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-09A1-061.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"license":"proprietary","keywords":["NASA","MODIS","Satellite","Imagery","Global","Reflectance","MOD09A1","MYD09A1"],"providers":[{"url":"https://lpdaac.usgs.gov/","name":"NASA LP DAAC at the USGS EROS Center","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["aqua","terra"],"instruments":["modis"]},"description":"The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. 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The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n| Name   | Scale                    | Period    | Number of basins |\n|--------|--------------------------|-----------|------------------|\n| ERA5   | Global                   | 1967-2020 | 3236             |\n| CHIRPS | Global (+/- 50 latitude) | 1981-2020 | 2951             |\n| EOBS   | Europe/North Africa      | 1979-2020 | 682              |\n| NLDAS  | USA                      | 1979-2020 | 1090             |\n| BOM    | Australia                | 1979-2020 | 116              |\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).","item_assets":{"data":{"type":"application/x-netcdf","roles":["data"],"title":"Flood Map","description":"Inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level."},"index":{"type":"application/json","roles":["index"],"title":"Index file","description":"Kerchunk index file."}},"stac_version":"1.0.0","cube:variables":{"P":{"type":"data","unit":"mm per day","attrs":{"units":"mm per day","description":"Average precipitation upstream of reservoir"},"dimensions":["time","GrandID","ksathorfrac"],"description":"Average precipitation upstream of reservoir"},"ETa":{"type":"data","unit":"mm per day","attrs":{"units":"mm per day","description":"Average 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The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.","item_assets":{"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"ET_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Total of Evapotranspiration","raster:bands":[{"unit":"kg/m^2/year","scale":0.1,"data_type":"uint16","spatial_resolution":500}]},"LE_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Average of Latent Heat Flux","raster:bands":[{"unit":"J/m^2/day","scale":10000,"data_type":"int16","spatial_resolution":500}]},"PET_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Total Potential Evapotranspiration","raster:bands":[{"unit":"kg/m^2/year","scale":0.1,"data_type":"uint16","spatial_resolution":500}]},"PLE_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Average of Potential Latent Heat Flux","raster:bands":[{"unit":"J/m^2/day","scale":10000,"data_type":"int16","spatial_resolution":500}]},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"ET_QC_500m":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Evapotranspiration Quality Assessment","raster:bands":[{"unit":"Percent","data_type":"uint8","spatial_resolution":500}]}},"stac_version":"1.0.0","msft:group_id":"modis","msft:container":"modis-061","stac_extensions":["https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.5067/MODIS/MOD16A3GF.061","citation":"Running, S., Mu, Q., Zhao, M., &amp; Moreno, A. 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Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).","item_assets":{"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"QC_Day":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Day Quality Control","raster:bands":[{"data_type":"uint8","spatial_resolution":1000}]},"Emis_29":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Average Day/Night Band 29 emissivity","raster:bands":[{"scale":0.002,"data_type":"uint8","spatial_resolution":1000}]},"Emis_31":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Average Day/Night Band 31 emissivity","raster:bands":[{"scale":0.002,"data_type":"uint8","spatial_resolution":1000}]},"Emis_32":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Average Day/Night Band 32 emissivity","raster:bands":[{"scale":0.002,"data_type":"uint8","spatial_resolution":1000}]},"QC_Night":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Night Quality Control","raster:bands":[{"data_type":"uint8","spatial_resolution":1000}]},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"LST_Day_1KM":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Day Land Surface Temperature","raster:bands":[{"unit":"Kelvin","scale":0.02,"data_type":"uint16","spatial_resolution":1000}]},"LST_Night_1KM":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Night Land Surface Temperature","raster:bands":[{"unit":"Kelvin","scale":0.02,"data_type":"uint16","spatial_resolution":1000}]},"View_Time_Day":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Day Time of Observation","raster:bands":[{"unit":"Hours","scale":0.1,"data_type":"uint8","spatial_resolution":1000}]},"View_Angle_Day":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Day view zenith angle","raster:bands":[{"unit":"Degree","data_type":"uint8","spatial_resolution":1000}]},"View_Time_Night":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Night Time of Observation","raster:bands":[{"unit":"Hours","scale":0.1,"data_type":"uint8","spatial_resolution":1000}]},"View_Angle_Night":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Night view zenith angle","raster:bands":[{"unit":"Degree","data_type":"uint8","spatial_resolution":1000}]}},"stac_version":"1.0.0","msft:group_id":"modis","msft:container":"modis-061","stac_extensions":["https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.5067/MODIS/MOD21A2.061","citation":"Hulley, G., &amp; Hook, S. (2021). <i>MODIS/Terra Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD21A2.061"},{"doi":"10.5067/MODIS/MYD21A2.061","citation":"Hulley, G. (2021). <i>MODIS/Aqua Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061</i> [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD21A2.061"}],"msft:storage_account":"modiseuwest","msft:short_description":"MODIS Land Surface Temperature/3-Band Emissivity 8-Day","msft:region":"westeurope"},{"id":"us-census","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census"},{"rel":"license","href":"https://www.census.gov/about/policies/open-gov/open-data.html","type":"text/html","title":"U.S. Census Open Data Policy"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/us-census","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"US Census","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/us-census.png","type":"image/png","title":"US Census"},"geoparquet-items":{"href":"abfs://items/us-census.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-124.763068,24.523096,-66.949895,49.384358],[-179.148909,51.214183,-129.974167,71.365162],[172.461667,51.357688,179.77847,53.01075],[-178.334698,18.910361,-154.806773,28.402123],[144.618068,13.234189,144.956712,13.654383],[-67.945404,17.88328,-65.220703,18.515683],[144.886331,14.110472,146.064818,20.553802],[-65.085452,17.673976,-64.564907,18.412655],[-171.089874,-14.548699,-168.1433,-11.046934],[-178.334698,18.910361,-154.806773,28.402123]]},"temporal":{"interval":[["2021-08-01T00:00:00Z","2021-08-01T00:00:00Z"]]}},"license":"proprietary","keywords":["US Census Bureau","Administrative boundaries","Population","Demographics"],"providers":[{"url":"https://www.census.gov/en.html","name":"United States Census Bureau","roles":["producer","licensor","processor"]},{"url":"https://makepath.com/","name":"makepath","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. 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These block groups can extend over multiple AIRs and ORTLs due to areas not meeting Block Group minimum population thresholds.","msft:item_name":"cb_2020_us_tbg_500k"},{"name":"Tribal Census Tracts (TTRACT)","description":"This file includes data on [Tribal Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_27) which are relatively small statistical subdivisions of AIRs and ORTLs defined by federally recognized tribal government officials in partnership with the Census Bureau. Due to population thresholds, the Tracts may consist of multiple non-contiguous areas.","msft:item_name":"cb_2020_us_ttract_500k"},{"name":"Census Block Groups (BG)","description":"This file contains data on [Census Block Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4). These groups are the second smallest geographic grouping. They consist of clusters of blocks within the same census tract that share the same first digit of their 4-character census block number. Census Block Groups generally contain between 600 and 3,000 people and generally cover contiguous areas.","msft:item_name":"cb_2020_us_bg_500k"},{"name":"Census Tracts (TRACT)","description":"This file contains data on [Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13) which are small and relatively permanent statistical subdivisions of a county or equivalent entity. Tract population size is generally between 1,200 and 8,000 people with an ideal size of 4,000. Boundaries tend to follow visible and identifiable features and are usually contiguous areas.","msft:item_name":"cb_2020_us_tract_500k"},{"name":"Congressional Districts: 116th Congress (CD116)","description":"This file contains data on the [Congressional Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_9) for the 116th Congress. ","msft:item_name":"cb_2020_us_cd116_500k"},{"name":"Consolidated Cities (CONCITY)","description":"This file contains data on [Consolidated Cities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_8). These are areas where one or several other incorporated places in a county or Minor Civil Division are included in a consolidated government but still exist as separate legal entities.","msft:item_name":"cb_2020_us_concity_500k"},{"name":"Counties (COUNTY)","description":"This file contains data on [Counties and Equivalent Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_12). These are the primary legal divisions of states. Most states use the term \"counties,\" but other terms such as \"Parishes,\" \"Municipios,\" or \"Independent Cities\" may be used. ","msft:item_name":"cb_2020_us_county_500k"},{"name":"Counties within Congressional Districts: 116th Congress (COUNTY_within_CD116)","description":"This file contains data on Counties within Congressional Districts.","msft:item_name":"cb_2020_us_county_within_cd116_500k"},{"name":"County Subdivisions (COUSUB)","description":"This file contains [County Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_11), which are the primary divisions of counties and equivalent entities. These divisions vary from state to state and include Barrios, Purchases, Townships, and other types of legal and statistical entities. ","msft:item_name":"cb_2020_us_cousub_500k"},{"name":"Divisions (DIVISION)","description":"This file contains data on [Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10) of the US. This file is similar to the Regions file but contains more divisions and encompasses several states per division.","msft:item_name":"cb_2020_us_division_500k"},{"name":"Core Based Statistical Areas (CBSAs)","description":"This file contains data on [Core Based Statistical Areas (CBSAs)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7). This encompasses all metropolitan and micropolitan statistical areas.","msft:item_name":"cb_2020_us_cbsa_500k"},{"name":"Combined Statistical Areas (CSA)","description":"This file contains data on [Combined Statistical Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which are areas that consist of two or more adjacent CBSAs that have significant employment interchanges.","msft:item_name":"cb_2020_us_csa_500k"},{"name":"Metropolitan Divisions (METDIV)","description":"This file contains data on [Metropolitan Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7). These areas are groupings of counties or equivalent entities within a metropolitan statistical area with a core of 2.5 million inhabitants and one or more main counties that represent employment centers, plus adjacent counties with commuting ties.","msft:item_name":"cb_2020_us_metdiv_500k"},{"name":"New England City and Town Areas (NECTA)","description":"This file contains [New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which encompass metropolitan and micropolitan statistical areas and urban clusters in New England.","msft:item_name":"cb_2020_us_necta_500k"},{"name":"New England City and Town Area Division (NECTADIV)","description":"This file contains [New England City and Town Areas Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which are smaller groupings of cities and towns in New England that contain a single core of 2.5 million inhabitants. Each division must have a total population of 100,000 or more.","msft:item_name":"cb_2020_us_nectadiv_500k"},{"name":"Combined New England City and Town Areas (CNECTA)","description":"This file contains data on [Combined New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), consisting of two or more adjacent NECTAs that have significant employment interchanges.","msft:item_name":"cb_2020_us_cnecta_500k"},{"name":"Places (PLACE)","description":"This file contains [Places](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_14) which are Incorporated Places (legal entities) and Census Designated Places (CDPs, statistical entities). An incorporated place usually is a city, town, village, or borough but can have other legal descriptions. CDPs are settled concentrations of population that are identifiable by name but are not legally incorporated.","msft:item_name":"cb_2020_us_place_500k"},{"name":"Regions (REGION)","description":"This file contains [Regions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10) of the US and encompasses several states per division.","msft:item_name":"cb_2020_us_region_500k"},{"name":"School Districts - Elementary (ELSD)","description":"This file contains [Elementary School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts with elementary schools.","msft:item_name":"cb_2020_us_elsd_500k"},{"name":"School Districts - Secondary (SCSD)","description":"This file contains [Secondary School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts with secondary schools.","msft:item_name":"cb_2020_us_scsd_500k"},{"name":"School Districts - Unified (UNSD)","description":"This file contains [Unified School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts that provide education to children of all school ages. Unified school districts can have both secondary and elementary schools.","msft:item_name":"cb_2020_us_unsd_500k"},{"name":"State Legislative Districts - Lower Chamber (SLDL)","description":"This file contains [Lower Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).","msft:item_name":"cb_2020_us_sldl_500k"},{"name":"State Legislative Districts - Upper Chamber (SLDU)","description":"This file contains [Upper Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).","msft:item_name":"cb_2020_us_sldu_500k"},{"name":"States (STATE)","description":"This file contains the [US States and State Equivalent Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_25). Within Census Bureau datasets, the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the US Virgin Islands) are treated as statistical equivalents of states alongside the 50 US states.","msft:item_name":"cb_2020_us_state_500k"},{"name":"Subbarrios (SUBBARRIO)","description":"This file contains [Subbarrios](https://www.census.gov/programs-surveys/geography/about/glossary.html#pr), which are legally defined subdivisions of Minor Civil Division in Puerto Rico. They don\"t exist within every Minor Civil Division and don\"t always cover the entire Minor Civil Division where they do exist.","msft:item_name":"cb_2020_72_subbarrio_500k"},{"name":"United States Outline","description":"This file contains the [United States Outline](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_30) shapefile. This contains all 50 US states plus the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the US Virgin Islands). There is only one feature within this dataset.","msft:item_name":"cb_2020_us_nation_5m"},{"name":"Voting Districts (VTD)","description":"This file contains all [US Voting Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_31), which are geographic features established by state, local and tribal governments to conduct elections.","msft:item_name":"cb_2020_us_vtd_500k"}],"msft:container":"us-census","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"United States population counts at various levels of cartographic aggregation","msft:region":"westeurope"},{"id":"jrc-gsw","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw"},{"rel":"cite-as","href":"https://doi.org/10.1038/nature20584"},{"rel":"license","href":"https://www.copernicus.eu/en/about-copernicus/international-cooperation","title":"Copernicus Open Access Policy"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/jrc-gsw","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"JRC Global Surface Water","assets":{"guide":{"href":"https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf","type":"application/pdf","roles":["metadata"],"title":"Users Guide","description":"Data users guide and description of the JRC GSW datasets"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/jrc-gsw.png","title":"European Commission Joint Research Centre - Global Surface Water","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/jrc-gsw.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-56.0,180.0,78.0]]},"temporal":{"interval":[["1984-03-01T00:00:00Z","2020-12-31T11:59:59Z"]]}},"license":"proprietary","sci:doi":"10.1038/nature20584","keywords":["Global","Water","Landsat"],"providers":[{"url":"https://global-surface-water.appspot.com/","name":"European Commission Joint Research Centre","roles":["producer","processor"]},{"url":"https://developers.google.com/earth-engine/datasets/tags/jrc","name":"Google","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"description":"Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery.  Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020.  Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n","item_assets":{"change":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Occurrence Change Intensity","description":"Change in water occurrence between the two periods (1984-1999) and (2000-2020)"},"extent":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Maximum Water Extent","description":"Binary indicator of whether water was ever present (from 1984-2020)","file:values":[{"values":[0],"summary":"Not water"},{"values":[1],"summary":"Water"}]},"occurrence":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Occurrence","description":"Frequency with which water was present from March 1984 to December 2020"},"recurrence":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Recurrence","description":"Frequency with which water returns from year to year"},"seasonality":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Seasonality","description":"Number of months that water was present from January 2020 to December 2020"},"transitions":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Transitions","description":"Categorical change in surface water status from 1984 to 2020","file:values":[{"values":[1],"summary":"Permanent"},{"values":[2],"summary":"New permanent"},{"values":[3],"summary":"Lost permanent"},{"values":[4],"summary":"Seasonal"},{"values":[5],"summary":"New seasonal"},{"values":[6],"summary":"Lost seasonal"},{"values":[7],"summary":"Seasonal to permanent"},{"values":[8],"summary":"Permanent to seasonal"},{"values":[9],"summary":"Ephemeral permanent"},{"values":[10],"summary":"Ephemeral seasonal"}]}},"sci:citation":"Jean-Francois Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping of global surface water and its long-term changes. Nature 540, 418-422 (2016)","stac_version":"1.0.0","msft:container":"jrc-gsw","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Global surface water distribution and dynamics from 1984 to 2020","msft:region":"westeurope"},{"id":"deltares-floods","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-floods/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-floods"},{"rel":"documentation","href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf","type":"application/pdf","title":"User Guide"},{"rel":"license","href":"https://cdla.dev/permissive-1-0/","type":"text/html","title":"Community Data License Agreement - Permissive, Version 1.0"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-floods/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/deltares-floods","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Deltares Global Flood Maps","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/deltares-flood.png","type":"image/png","roles":["thumbnail"],"title":"Thumbnail"}},"extent":{"spatial":{"bbox":[[-180.0,90.0,180.0,-90.0]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2018-12-31T00:00:00Z"],["2050-01-01T00:00:00Z","2050-12-31T00:00:00Z"]]}},"license":"CDLA-Permissive-1.0","keywords":["Deltares","Flood","Sea level rise","Water","Global"],"providers":[{"url":"https://www.deltares.nl/en/","name":"Deltares","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"summaries":{"deltares:dem_name":["NASADEM","MERITDEM","LIDAR"],"deltares:resolution":["90m","1km","5km"],"deltares:return_period":[0,2,5,10,25,50,100,250],"deltares:sea_level_year":[2018,2050]},"description":"[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a ‘bathtub’ like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5 m km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n<https://deltaresfloodssa.blob.core.windows.net/floods/v2021.06/events/MERITDEM_90m-wm_final/Omar_2008_masked.nc>\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).","item_assets":{"data":{"type":"application/x-netcdf","roles":["data"],"title":"Flood Map","description":"Inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level."},"index":{"type":"application/json","roles":["index"],"title":"Index file","description":"Kerchunk index file."}},"stac_version":"1.0.0","cube:variables":{"inun":{"type":"data","unit":"m","attrs":{"units":"m","long_name":"Coastal flooding","standard_name":"water_surface_height_above_reference_datum"},"dimensions":["time","lat","lon"],"description":"Coastal flooding"},"projection":{"type":"data","attrs":{"EPSG_code":"EPSG:4326","long_name":"wgs84","proj4_params":"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs","grid_mapping_name":"latitude_longitude"},"shape":[],"dimensions":[],"description":"wgs84"}},"msft:container":"floods","cube:dimensions":{"lat":{"axis":"y","type":"spatial","extent":[-89.97500000000002,89.975],"description":"latitude","reference_system":{"name":"undefined","type":"GeographicCRS","datum":{"id":{"code":6326,"authority":"EPSG"},"name":"World Geodetic System 1984","type":"GeodeticReferenceFrame","ellipsoid":{"name":"WGS 84","semi_major_axis":6378137,"inverse_flattening":298.257223563}},"$schema":"https://proj.org/schemas/v0.4/projjson.schema.json","coordinate_system":{"axis":[{"name":"Longitude","unit":"degree","direction":"east","abbreviation":"lon"},{"name":"Latitude","unit":"degree","direction":"north","abbreviation":"lat"}],"subtype":"ellipsoidal"}}},"lon":{"axis":"x","type":"spatial","extent":[-179.975,179.97500000000005],"description":"longitude","reference_system":{"name":"undefined","type":"GeographicCRS","datum":{"id":{"code":6326,"authority":"EPSG"},"name":"World Geodetic System 1984","type":"GeodeticReferenceFrame","ellipsoid":{"name":"WGS 84","semi_major_axis":6378137,"inverse_flattening":298.257223563}},"$schema":"https://proj.org/schemas/v0.4/projjson.schema.json","coordinate_system":{"axis":[{"name":"Longitude","unit":"degree","direction":"east","abbreviation":"lon"},{"name":"Latitude","unit":"degree","direction":"north","abbreviation":"lat"}],"subtype":"ellipsoidal"}}},"time":{"type":"temporal","extent":["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"],"description":"time"}},"stac_extensions":["https://stac-extensions.github.io/datacube/v2.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"deltaresfloodssa","msft:short_description":"Global estimates of coastal inundation under various sea level rise conditions and return periods.","msft:region":"westeurope"},{"id":"modis-43A4-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-43A4-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-43A4-061"},{"rel":"help","href":"https://www.umb.edu/spectralmass/terra_aqua_modis/v006","title":"User Guide"},{"rel":"describedby","href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A4","title":"MCD43A4 file specification"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MCD43A4.061","title":"LP DAAC - 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The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.","item_assets":{"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"Nadir_Reflectance_Band1":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","reflectance"],"title":"NBAR at local solar noon for Band 1","eo:bands":[{"name":"Nadir_Reflectance_Band1","common_name":"red","center_wavelength":0.645,"full_width_half_max":0.5}],"raster:bands":[{"scale":0.0001,"data_type":"int16","spatial_resolution":500}]},"Nadir_Reflectance_Band2":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","reflectance"],"title":"NBAR at local solar noon for Band 2","eo:bands":[{"name":"Nadir_Reflectance_Band2","common_name":"nir08","center_wavelength":0.8585,"full_width_half_max":0.35}],"raster:bands":[{"scale":0.0001,"data_type":"int16","spatial_resolution":500}]},"Nadir_Reflectance_Band3":{"type":"image/tiff; 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Canada"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nrcan-landcover/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/nrcan-landcover","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Land Cover of Canada","assets":{"metadata":{"href":"https://open.canada.ca/data/en/dataset/4e615eae-b90c-420b-adee-2ca35896caf6.jsonld","type":"application/json","roles":["metadata"],"title":"Land cover of Canada metadata"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/nrcan-landcover.png","type":"image/png","roles":["thumbnail"],"title":"Land cover of Canada thumbnail"},"geoparquet-items":{"href":"abfs://items/nrcan-landcover.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC 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Canada","roles":["licensor","processor","producer"]},{"url":"https://sparkgeo.com","name":"Sparkgeo","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"proj:epsg":[3978],"label:type":["raster"],"label:tasks":["classification"],"label:classes":[{"name":"","classes":["Temperate or sub-polar needleleaf forest","Sub-polar taiga needleleaf forest","Temperate or sub-polar broadleaf deciduous forest","Mixed forest","Temperate or sub-polar shrubland","Temperate or sub-polar grassland","Sub-polar or polar shrubland-lichen-moss","Sub-polar or polar grassland-lichen-moss","Sub-polar or polar barren-lichen-moss","Wetland","Cropland","Barren lands","Urban","Water","Snow and Ice"]}]},"description":"Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.","item_assets":{"metadata":{"type":"application/json","roles":["metadata"],"title":"Land cover of Canada metadata"},"landcover":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","labels","labels-raster"],"title":"Land cover of Canada COG","proj:epsg":3978,"file:values":[{"values":[1],"summary":"Temperate or sub-polar needleleaf forest"},{"values":[2],"summary":"Sub-polar taiga needleleaf forest"},{"values":[5],"summary":"Temperate or sub-polar broadleaf deciduous forest"},{"values":[6],"summary":"Mixed forest"},{"values":[8],"summary":"Temperate or sub-polar shrubland"},{"values":[10],"summary":"Temperate or sub-polar grassland"},{"values":[11],"summary":"Sub-polar or polar shrubland-lichen-moss"},{"values":[12],"summary":"Sub-polar or polar grassland-lichen-moss"},{"values":[13],"summary":"Sub-polar or polar barren-lichen-moss"},{"values":[14],"summary":"Wetland"},{"values":[15],"summary":"Cropland"},{"values":[16],"summary":"Barren lands"},{"values":[17],"summary":"Urban"},{"values":[18],"summary":"Water"},{"values":[19],"summary":"Snow and Ice"}],"raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]}},"stac_version":"1.0.0","msft:container":"landcover","stac_extensions":["https://stac-extensions.github.io/label/v1.0.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"nrcanlandcovereuwest","msft:short_description":"Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.","msft:region":"westeurope"},{"id":"modis-10A2-061","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MOD10A2.061","title":"MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61"},{"rel":"cite-as","href":"https://doi.org/10.5067/MODIS/MYD10A2.061","title":"MODIS/Aqua Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61"},{"rel":"license","href":"https://nsidc.org/data/data-programs/nsidc-daac/citing-nsidc-daac","title":"Use and Copyright | National Snow and Ice Data Center"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/modis-10A2-061","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"MODIS Snow Cover 8-day","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-10A2-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Snow Cover 8-day thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-10A2-061.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z",null]]}},"license":"proprietary","keywords":["NASA","MODIS","Satellite","Global","Snow","MOD10A2","MYD10A2"],"providers":[{"url":"https://nsidc.org","name":"National Snow and Ice Data Center","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"platform":["aqua","terra"],"instruments":["modis"]},"description":"This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.","item_assets":{"hdf":{"type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"metadata":{"type":"application/xml","roles":["metadata"],"title":"Federal Geographic Data Committee (FGDC) Metadata"},"Maximum_Snow_Extent":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Maximum snow extent over the 8-day period.","raster:bands":[{"data_type":"uint8","spatial_resolution":500}],"classification:classes":[{"value":0,"description":"missing data"},{"value":1,"description":"no decision"},{"value":11,"description":"night"},{"value":25,"description":"no snow"},{"value":37,"description":"lake"},{"value":39,"description":"ocean"},{"value":50,"description":"cloud"},{"value":100,"description":"lake ice"},{"value":200,"description":"snow"},{"value":254,"description":"detector saturated"}]},"Eight_Day_Snow_Cover":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Snow chronology bit flags for each day in the eight-day observation period.","raster:bands":[{"data_type":"uint8","spatial_resolution":500}]}},"stac_version":"1.0.0","msft:group_id":"modis","msft:container":"modis-061","stac_extensions":["https://stac-extensions.github.io/classification/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.5067/MODIS/MOD10A2.061","citation":"Hall, D. K., &amp; Riggs, G. A. (2021). <i>MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 61</i> [Data set]. NASA National Snow and Ice Data Center DAAC. https://doi.org/10.5067/MODIS/MOD10A2.061"},{"doi":"10.5067/MODIS/MYD10A2.061","citation":"Hall, D. K., &amp; Riggs, G. A. (2021). <i>MODIS/Aqua Snow Cover 8-Day L3 Global 500m Grid, Version 61</i> [Data set]. NASA National Snow and Ice Data Center DAAC. https://doi.org/10.5067/MODIS/MYD10A2.061"}],"msft:storage_account":"modiseuwest","msft:short_description":"MODIS Snow Cover 8-day","msft:region":"westeurope"},{"id":"ecmwf-forecast","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"CC-BY-4.0 license"},{"rel":"documentation","href":"https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time","type":"text/html","title":"ECMWF Open Data (Real Time) documentation"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ECMWF Open Data (real-time)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/ecmwf-forecast-thumbnail-proj.png","type":"image/png","roles":["thumbnail"],"title":"thumbnail"}},"extent":{"spatial":{"bbox":[[-180,90,180,-90]]},"temporal":{"interval":[[null,null]]}},"license":"CC-BY-4.0","keywords":["ECMWF","forecast","weather"],"providers":[{"url":"https://www.ecmwf.int/","name":"ECMWF","roles":["producer"]}],"summaries":{"ecmwf:types":["ef","ep","fc","tf"],"ecmwf:streams":["enfo","mmsf","oper","scda","scwv","waef","wave"],"ecmwf:pressure_levels":[1000,925,850,700,500,300,250,200,50],"ecmwf:reference_times":["00","06","12","18"]},"description":"The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n<a href=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\"><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/ecmwf-forecast-coverage.png\" width=\"100%\"/></a>\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows *forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* **stream** is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n  * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n  * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n  * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n  * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n  * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n  * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n  * `wave`: wave model\n* **type** is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n  * `fc`: forecast\n  * `ef`: ensemble forecast\n  * `pf`: ensemble probabilities\n  * `tf`: trajectory forecast for tropical cyclone tracks\n* **reference time** is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the *reference time* changes and `step` as the *forecast time* changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as `<value><unit>`, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast *was made*, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"© [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).","item_assets":{"data":{"type":"application/wmo-GRIB2","roles":["data"],"title":"GRIB2 data file","description":"The forecast data, as a grib2 file. Subsets of the data can be loaded using information from the associated index file."},"index":{"type":"application/x-ndjson","roles":["index"],"title":"Index file","description":"The index file contains information on each message within the GRIB2 file."}},"msft:region":"westeurope","stac_version":"1.0.0","msft:container":"ecmwf","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"ECMWF Open Data (Real Time) forecasts"},{"id":"noaa-mrms-qpe-24h-pass2","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2"},{"rel":"license","href":"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf","type":"application/pdf","title":"MRMS Dataset Sharing Policy"},{"rel":"about","href":"https://mrms.nssl.noaa.gov","type":"text/html","title":"MRMS Homepage"},{"rel":"about","href":"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881","type":"text/html","title":"MRMS QPE Technical Product Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-24h-pass2","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA MRMS QPE 24-Hour Pass 2","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-24h-pass2-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA MRMS QPE 24-Hour Pass 2 Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-mrms-qpe-24h-pass2.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"QS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-176.0,9.0,150.0,72.0],[-130.0,20.0,-60.0,55.0],[-164.0,15.0,-151.0,26.0],[140.0,9.0,150.0,18.0],[-176.0,50.0,-126.0,72.0],[-90.0,10.0,-60.0,25.0]]},"temporal":{"interval":[["2022-07-21T20:00:00Z",null]]}},"license":"proprietary","keywords":["NOAA","MRMS","QPE","Precipitation","Weather","United States","Guam","Caribbean"],"providers":[{"url":"https://www.nssl.noaa.gov/projects/mrms","name":"NOAA National Severe Storms Laboratory","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_mrms_qpe:pass":[2],"noaa_mrms_qpe:period":[24],"noaa_mrms_qpe:region":["CONUS","HAWAII","GUAM","ALASKA","CARIB"]},"description":"The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).","item_assets":{"cog":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Processed Cloud Optimized GeoTIFF file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]},"grib2":{"type":"application/wmo-GRIB2","roles":["data"],"title":"Original GRIB2 file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]}},"stac_version":"1.0.0","msft:group_id":"noaa-mrms-qpe","msft:container":"mrms-cogs","stac_extensions":["https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json"],"msft:storage_account":"mrms","msft:short_description":"Integrated multi-sensor cumulative precipitation estimate for the past 24 hours with a 2-hour latency.","msft:region":"westeurope"},{"id":"nasadem","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"Public Domain"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/nasadem","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NASADEM HGT v001","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/nasadem.png","type":"image/png","roles":["thumbnail"],"title":"NASADEM"},"geoparquet-items":{"href":"abfs://items/nasadem.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-179.000139,-56.000139,179.000139,61.000139]]},"temporal":{"interval":[["2000-02-20T00:00:00Z","2000-02-20T00:00:00Z"]]}},"license":"proprietary","keywords":["NASA","JPL","Elevation","DEM","USGS","NGA","SRTM"],"providers":[{"url":"https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem","name":"NASA","roles":["producer","licensor"]},{"url":"https://trs.jpl.nasa.gov/handle/2014/46123","name":"JPL","roles":["producer","licensor"]},{"url":"https://lpdaac.usgs.gov/products/nasadem_hgtv001/","name":"USGS","roles":["producer","licensor"]},{"url":"https://doi.org/10.5069/G93T9FD9","name":"OpenTopography","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[30]},"description":"[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n","item_assets":{"elevation":{"type":"image/tiff; 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This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution.  This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification.  The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Global land cover data","file:values":[{"values":[0],"summary":"No Data"},{"values":[1],"summary":"Water"},{"values":[2],"summary":"Trees"},{"values":[3],"summary":"Grass"},{"values":[4],"summary":"Flooded vegetation"},{"values":[5],"summary":"Crops"},{"values":[6],"summary":"Scrub/shrub"},{"values":[7],"summary":"Built area"},{"values":[8],"summary":"Bare 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These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. 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DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"DRCOG LULC","description":"Denver Regional Council of Governments (DRCOG) Land Use Land Cover (LULC) Classifications","raster:bands":[{"unit":"usft","sampling":"area","data_type":"uint8","description":"Classification values"}],"classification:classes":[{"value":1,"color_hint":"FF0000","description":"Structures"},{"value":2,"color_hint":"B2B2B2","description":"Impervious Surfaces"},{"value":3,"color_hint":"00A9E6","description":"Water"},{"value":4,"color_hint":"C7D79E","description":"Prairie/Grassland/Natural Ground Cover"},{"value":5,"color_hint":"267300","description":"Tree Canopy"},{"value":6,"color_hint":"70A800","description":"Turf/Irrigated Land"},{"value":7,"color_hint":"FFEBAF","description":"Barren Land"},{"value":8,"color_hint":"FFAE42","description":"Cropland"}]}},"stac_version":"1.0.0","msft:container":"drcog-lulc","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.1.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"2018 and 2020 Land Use/Land Cover classification of the Denver, Colorado region.","msft:region":"westeurope"},{"id":"chesapeake-lc-7","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-7/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-7"},{"rel":"describedby","href":"https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf","type":"application/pdf","title":"User Guide"},{"rel":"license","href":"https://www.chesapeakeconservancy.org/conservation-innovation-center/precision-conservation/chesapeake-bay-program/","type":"text/html","title":"Data Availability Description"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-7/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/chesapeake-lc-7","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Chesapeake Land Cover (7-class)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chesapeake-lc-7-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Chesapeake Land Cover (7-class) Thumbnail"}},"extent":{"spatial":{"bbox":[[-81.14658496196135,36.21291717905733,-73.27357561029186,44.77821441524524]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["Land Cover","Chesapeake Bay Watershed","Chesapeake Conservancy"],"providers":[{"url":"https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/","name":"Chesapeake Conservancy Conservation Innovation Center","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Land Cover Classes","raster:bands":[{"nodata":15,"sampling":"area","data_type":"uint8","spatial_resolution":1}],"classification:classes":[{"value":1,"color-hint":"00C5FF","description":"Water"},{"value":2,"color-hint":"267300","description":"Tree Canopy and Shrubs"},{"value":3,"color-hint":"A3FF73","description":"Low Vegetation"},{"value":4,"color-hint":"FFAA00","description":"Barren"},{"value":5,"color-hint":"9C9C9C","description":"Impervious Surfaces"},{"value":6,"color-hint":"000000","description":"Impervious Roads"},{"value":7,"color-hint":"C500FF","description":"Aberdeen Proving Ground"}]}},"stac_version":"1.0.0","msft:group_id":"chesapeake-lulc","msft:container":"chesapeake","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"Chesapeake Bay Watershed land cover product with 7 classes at 1m resolution.","msft:region":"westeurope"},{"id":"chesapeake-lc-13","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-13/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-13"},{"rel":"describedby","href":"https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf","type":"application/pdf","title":"User Guide"},{"rel":"describedby","href":"https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf","type":"application/pdf","title":"Class Descriptions"},{"rel":"license","href":"https://www.chesapeakeconservancy.org/conservation-innovation-center/precision-conservation/chesapeake-bay-program/","type":"text/html","title":"Data Availability Description"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lc-13/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/chesapeake-lc-13","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Chesapeake Land Cover (13-class)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chesapeake-lc-13-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Chesapeake Land Cover (13-class) Thumbnail"}},"extent":{"spatial":{"bbox":[[-81.14658496196135,36.21291717905733,-73.27357561029186,44.77821441524524]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["Land Cover","Chesapeake Bay Watershed","Chesapeake Conservancy"],"providers":[{"url":"https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/","name":"Chesapeake Conservancy Conservation Innovation Center","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Land Cover Classes","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":1}],"classification:classes":[{"value":1,"color-hint":"00C5FF","description":"Water"},{"value":2,"color-hint":"00A884","description":"Emergent Wetlands"},{"value":3,"color-hint":"267300","description":"Tree Canopy"},{"value":4,"color-hint":"4CE600","description":"Shrubland"},{"value":5,"color-hint":"A3FF73","description":"Low Vegetation"},{"value":6,"color-hint":"FFAA00","description":"Barren"},{"value":7,"color-hint":"FF0000","description":"Structures"},{"value":8,"color-hint":"9C9C9C","description":"Impervious Surfaces"},{"value":9,"color-hint":"000000","description":"Impervious Roads"},{"value":10,"color-hint":"737300","description":"Tree Canopy over Structures"},{"value":11,"color-hint":"E6E600","description":"Tree Canopy over Impervious Surfaces"},{"value":12,"color-hint":"FFFF73","description":"Tree Canopy over impervious Roads"},{"value":13,"color-hint":"C500FF","description":"Aberdeen Proving Ground"}]}},"stac_version":"1.0.0","msft:group_id":"chesapeake-lulc","msft:container":"chesapeake","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"Chesapeake Bay Watershed land cover product with 13 classes at 1m resolution.","msft:region":"westeurope"},{"id":"chesapeake-lu","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lu/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lu"},{"rel":"describedby","href":"https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf","type":"application/pdf","title":"Class Descriptions"},{"rel":"license","href":"https://www.chesapeakeconservancy.org/conservation-innovation-center/precision-conservation/chesapeake-bay-program/","type":"text/html","title":"Data Availability Description"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chesapeake-lu/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/chesapeake-lu","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Chesapeake Land Use","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chesapeake-lu-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Chesapeake Land Use Thumbnail"}},"extent":{"spatial":{"bbox":[[-81.14648244566828,36.18730972451623,-73.11082086653798,44.7781991560751]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["Land Use","Chesapeake Bay Watershed","Chesapeake Conservancy"],"providers":[{"url":"https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/","name":"Chesapeake Conservancy Conservation Innovation Center","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Land Use Classes","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":1}],"classification:classes":[{"value":1,"color-hint":"000000","description":"Impervious Roads"},{"value":2,"color-hint":"730000","description":"Impervious Non-Roads"},{"value":3,"color-hint":"55FF00","description":"Tree Canopy over Impervious Surfaces"},{"value":4,"color-hint":"0070FF","description":"Water"},{"value":5,"color-hint":"00FFC5","description":"Tidal Wetlands"},{"value":6,"color-hint":"00E6A9","description":"Floodplain Wetlands"},{"value":7,"color-hint":"00E6A9","description":"Other Wetlands"},{"value":8,"color-hint":"267300","description":"Forest"},{"value":9,"color-hint":"AAFF00","description":"Tree Canopy over Turf Grass"},{"value":10,"color-hint":"A87000","description":"Mixed Open"},{"value":11,"color-hint":"FFBEE8","description":"Fractional Turf (small)"},{"value":12,"color-hint":"FFBEE8","description":"Fractional Turf (medium)"},{"value":13,"color-hint":"FFBEE8","description":"Fractional Turf (large)"},{"value":14,"color-hint":"C500FF","description":"Fractional Impervious"},{"value":15,"color-hint":"FFFF73","description":"Turf Grass"},{"value":16,"color-hint":"E69800","description":"Cropland"},{"value":17,"color-hint":"E69800","description":"Pasture/Hay"}]}},"stac_version":"1.0.0","msft:group_id":"chesapeake-lulc","msft:container":"chesapeake","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"Chesapeake Bay Watershed land use product with 17 classes at 1m resolution.","msft:region":"westeurope"},{"id":"noaa-mrms-qpe-1h-pass1","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass1/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass1"},{"rel":"license","href":"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf","type":"application/pdf","title":"MRMS Dataset Sharing Policy"},{"rel":"about","href":"https://mrms.nssl.noaa.gov","type":"text/html","title":"MRMS Homepage"},{"rel":"about","href":"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881","type":"text/html","title":"MRMS QPE Technical Product Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass1/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-1h-pass1","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA MRMS QPE 1-Hour Pass 1","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-1h-pass1-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA MRMS QPE 1-Hour Pass 1 Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-mrms-qpe-1h-pass1.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"QS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-176.0,9.0,150.0,72.0],[-130.0,20.0,-60.0,55.0],[-164.0,15.0,-151.0,26.0],[140.0,9.0,150.0,18.0],[-176.0,50.0,-126.0,72.0],[-90.0,10.0,-60.0,25.0]]},"temporal":{"interval":[["2022-07-21T20:00:00Z",null]]}},"license":"proprietary","keywords":["NOAA","MRMS","QPE","Precipitation","Weather","United States","Guam","Caribbean"],"providers":[{"url":"https://www.nssl.noaa.gov/projects/mrms","name":"NOAA National Severe Storms Laboratory","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_mrms_qpe:pass":[1],"noaa_mrms_qpe:period":[1],"noaa_mrms_qpe:region":["CONUS","HAWAII","GUAM","ALASKA","CARIB"]},"description":"The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n","item_assets":{"cog":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Processed Cloud Optimized GeoTIFF file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]},"grib2":{"type":"application/wmo-GRIB2","roles":["data"],"title":"Original GRIB2 file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]}},"stac_version":"1.0.0","msft:group_id":"noaa-mrms-qpe","msft:container":"mrms-cogs","stac_extensions":["https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json"],"msft:storage_account":"mrms","msft:short_description":"Integrated multi-sensor cumulative precipation estimate for the past hour with a 1-hour latency.","msft:region":"westeurope"},{"id":"noaa-mrms-qpe-1h-pass2","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass2/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass2"},{"rel":"license","href":"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf","type":"application/pdf","title":"MRMS Dataset Sharing Policy"},{"rel":"about","href":"https://mrms.nssl.noaa.gov","type":"text/html","title":"MRMS Homepage"},{"rel":"about","href":"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881","type":"text/html","title":"MRMS QPE Technical Product Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass2/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-1h-pass2","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA MRMS QPE 1-Hour Pass 2","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-1h-pass2-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA MRMS QPE 1-Hour Pass 2 Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-mrms-qpe-1h-pass2.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"QS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-176.0,9.0,150.0,72.0],[-130.0,20.0,-60.0,55.0],[-164.0,15.0,-151.0,26.0],[140.0,9.0,150.0,18.0],[-176.0,50.0,-126.0,72.0],[-90.0,10.0,-60.0,25.0]]},"temporal":{"interval":[["2022-07-21T20:00:00Z",null]]}},"license":"proprietary","keywords":["NOAA","MRMS","QPE","Precipitation","Weather","United States","Guam","Caribbean"],"providers":[{"url":"https://www.nssl.noaa.gov/projects/mrms","name":"NOAA National Severe Storms Laboratory","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_mrms_qpe:pass":[2],"noaa_mrms_qpe:period":[1],"noaa_mrms_qpe:region":["CONUS","HAWAII","GUAM","ALASKA","CARIB"]},"description":"The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n","item_assets":{"cog":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Processed Cloud Optimized GeoTIFF file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]},"grib2":{"type":"application/wmo-GRIB2","roles":["data"],"title":"Original GRIB2 file","raster:bands":[{"unit":"mm","data_type":"float64","spatial_resolution":1000}]}},"stac_version":"1.0.0","msft:group_id":"noaa-mrms-qpe","msft:container":"mrms-cogs","stac_extensions":["https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json"],"msft:storage_account":"mrms","msft:short_description":"Integrated multi-sensor cumulative precipation estimate for the past hour with a 2-hour latency.","msft:region":"westeurope"},{"id":"noaa-nclimgrid-monthly","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5SX6B56","type":"text/html","title":"NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)"},{"rel":"license","href":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332#Constraints","type":"text/html","title":"NClimGrid Data Use and Access Constraints"},{"rel":"about","href":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332","type":"text/html","title":"Product Landing Page"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-nclimgrid-monthly","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-nclimgrid-monthly-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Monthly NOAA NClimGrid Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-nclimgrid-monthly.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-124.708333,24.541666,-66.999995,49.375001]]},"temporal":{"interval":[["1895-01-01T00:00:00Z",null]]}},"license":"proprietary","sci:doi":"10.7289/V5SX6B56","keywords":["United States","NOAA","NClimGrid","Climate","Precipitation","Temperature"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"NOAA National Centers for Environmental Information","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"description":"The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains **Monthly** data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*\n","item_assets":{"prcp":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Monthly Precipitation (mm)","raster:bands":[{"unit":"mm","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tavg":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Monthly Average Temperature (degree Celsius)","raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmax":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Monthly Maximmum Temperature (degree Celsius)","raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmin":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Monthly Minimum Temperature (degree Celsius)","raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]}},"sci:citation":"Vose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1. NOAA National Centers for Environmental Information. DOI:10.7289/V5SX6B56.","stac_version":"1.0.0","msft:group_id":"noaa-nclimgrid","msft:container":"nclimgrid-cogs","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json"],"sci:publications":[{"doi":"10.1175/JAMC-D-13-0248.1","citation":"Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C., Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251."}],"msft:storage_account":"nclimgridwesteurope","msft:short_description":"Gridded surface observations of monthly temperature and precipitation for the continental United States","msft:region":"westeurope"},{"id":"usda-cdl","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl"},{"rel":"about","href":"https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php","title":"Product Landing Page"},{"rel":"license","href":"https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#Section3_5.0","title":"Redistribution information"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/usda-cdl","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USDA Cropland Data Layers (CDLs)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usda-cdl-thumb.png","type":"image/png","roles":["thumbnail"],"title":"USDA Cropland Data Layer (CDL) thumbnail"}},"extent":{"spatial":{"bbox":[[-127.887212,22.94027,-65.345507,51.603492]]},"temporal":{"interval":[["2008-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["USDA","United States","Land Cover","Land Use","Agriculture"],"providers":[{"url":"https://www.nass.usda.gov/","name":"United States Department of Agriculture - National Agricultural Statistics Service","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"usda_cdl:type":["cropland","frequency","cultivated"]},"description":"The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).","item_assets":{"corn":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":255,"data_type":"uint8","spatial_resolution":30}]},"wheat":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":255,"data_type":"uint8","spatial_resolution":30}]},"cotton":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":255,"data_type":"uint8","spatial_resolution":30}]},"cropland":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":0,"data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"value":1,"color_hint":"FFD200","description":"Corn"},{"value":2,"color_hint":"FF2525","description":"Cotton"},{"value":3,"color_hint":"00A8E3","description":"Rice"},{"value":4,"color_hint":"FF9E0A","description":"Sorghum"},{"value":5,"color_hint":"256F00","description":"Soybeans"},{"value":6,"color_hint":"FFFF00","description":"Sunflower"},{"value":10,"color_hint":"6FA400","description":"Peanuts"},{"value":11,"color_hint":"00AE4A","description":"Tobacco"},{"value":12,"color_hint":"DDA40A","description":"Sweet Corn"},{"value":13,"color_hint":"DDA40A","description":"Pop or Orn Corn"},{"value":14,"color_hint":"7DD2FF","description":"Mint"},{"value":21,"color_hint":"E1007B","description":"Barley"},{"value":22,"color_hint":"886153","description":"Durum Wheat"},{"value":23,"color_hint":"D7B56B","description":"Spring Wheat"},{"value":24,"color_hint":"A46F00","description":"Winter Wheat"},{"value":25,"color_hint":"D59EBB","description":"Other Small Grains"},{"value":26,"color_hint":"6F6F00","description":"Winter Wheat/Soybeans"},{"value":27,"color_hint":"AC007B","description":"Rye"},{"value":28,"color_hint":"9F5888","description":"Oats"},{"value":29,"color_hint":"6F0048","description":"Millet"},{"value":30,"color_hint":"D59EBB","description":"Speltz"},{"value":31,"color_hint":"D1FF00","description":"Canola"},{"value":32,"color_hint":"7D99FF","description":"Flaxseed"},{"value":33,"color_hint":"D5D500","description":"Safflower"},{"value":34,"color_hint":"D1FF00","description":"Rape Seed"},{"value":35,"color_hint":"00AE4A","description":"Mustard"},{"value":36,"color_hint":"FFA4E1","description":"Alfalfa"},{"value":37,"color_hint":"A4F18B","description":"Other Hay/Non Alfalfa"},{"value":38,"color_hint":"00AE4A","description":"Camelina"},{"value":39,"color_hint":"D59EBB","description":"Buckwheat"},{"value":41,"color_hint":"A800E3","description":"Sugarbeets"},{"value":42,"color_hint":"A40000","description":"Dry Beans"},{"value":43,"color_hint":"6F2500","description":"Potatoes"},{"value":44,"color_hint":"00AE4A","description":"Other Crops"},{"value":45,"color_hint":"B07DFF","description":"Sugarcane"},{"value":46,"color_hint":"6F2500","description":"Sweet Potatoes"},{"value":47,"color_hint":"FF6666","description":"Misc. Vegs & Fruits"},{"value":48,"color_hint":"FF6666","description":"Watermelons"},{"value":49,"color_hint":"FFCC66","description":"Onions"},{"value":50,"color_hint":"FF6666","description":"Cucumbers"},{"value":51,"color_hint":"00AE4A","description":"Chick Peas"},{"value":52,"color_hint":"00DDAE","description":"Lentils"},{"value":53,"color_hint":"53FF00","description":"Peas"},{"value":54,"color_hint":"F1A277","description":"Tomatoes"},{"value":55,"color_hint":"FF6666","description":"Caneberries"},{"value":56,"color_hint":"00AE4A","description":"Hops"},{"value":57,"color_hint":"7DD2FF","description":"Herbs"},{"value":58,"color_hint":"E8BEFF","description":"Clover/Wildflowers"},{"value":59,"color_hint":"AEFFDD","description":"Sod/Grass Seed"},{"value":60,"color_hint":"00AE4A","description":"Switchgrass"},{"value":61,"color_hint":"BEBE77","description":"Fallow/Idle 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Forest"},{"value":143,"color_hint":"92CC92","description":"Mixed Forest"},{"value":152,"color_hint":"C5D59E","description":"Shrubland"},{"value":176,"color_hint":"E8FFBE","description":"Grassland/Pasture"},{"value":190,"color_hint":"7DB0B0","description":"Woody Wetlands"},{"value":195,"color_hint":"7DB0B0","description":"Herbaceous Wetlands"},{"value":204,"color_hint":"00FF8B","description":"Pistachios"},{"value":205,"color_hint":"D59EBB","description":"Triticale"},{"value":206,"color_hint":"FF6666","description":"Carrots"},{"value":207,"color_hint":"FF6666","description":"Asparagus"},{"value":208,"color_hint":"FF6666","description":"Garlic"},{"value":209,"color_hint":"FF6666","description":"Cantaloupes"},{"value":210,"color_hint":"FF8EAA","description":"Prunes"},{"value":211,"color_hint":"334833","description":"Olives"},{"value":212,"color_hint":"E36F25","description":"Oranges"},{"value":213,"color_hint":"FF6666","description":"Honeydew Melons"},{"value":214,"color_hint":"FF6666","description":"Broccoli"},{"value":215,"color_hint":"66994B","description":"Avocados"},{"value":216,"color_hint":"FF6666","description":"Peppers"},{"value":217,"color_hint":"B09A6F","description":"Pomegranates"},{"value":218,"color_hint":"FF8EAA","description":"Nectarines"},{"value":219,"color_hint":"FF6666","description":"Greens"},{"value":220,"color_hint":"FF8EAA","description":"Plums"},{"value":221,"color_hint":"FF6666","description":"Strawberreis"},{"value":222,"color_hint":"FF6666","description":"Squash"},{"value":223,"color_hint":"FF8EAA","description":"Apricots"},{"value":224,"color_hint":"00AE4A","description":"Vetch"},{"value":225,"color_hint":"FFD200","description":"Winter Wheat/Corn"},{"value":226,"color_hint":"FFD200","description":"Oats/Corn"},{"value":227,"color_hint":"FF6666","description":"Lettuce"},{"value":228,"color_hint":"FF6666","description":"Triticale/Corn"},{"value":229,"color_hint":"FF6666","description":"Pumpkins"},{"value":230,"color_hint":"886153","description":"Lettuce/Durum Wheat"},{"value":231,"color_hint":"FF6666","description":"Lettuce/Cataloupe"},{"value":232,"color_hint":"FF2525","description":"Lettuce/Cotton"},{"value":233,"color_hint":"A1007B","description":"Lettuce/Barley"},{"value":234,"color_hint":"FF9E0A","description":"Durum Wheat/Sorghum"},{"value":235,"color_hint":"FF9E0A","description":"Barley/Sorghum"},{"value":236,"color_hint":"A46F00","description":"Winter Wheat/Sorghum"},{"value":237,"color_hint":"FFD200","description":"Barley/Corn"},{"value":238,"color_hint":"A46F00","description":"Winter Wheat/Cotton"},{"value":239,"color_hint":"256F00","description":"Soybeans/Cotton"},{"value":240,"color_hint":"256F00","description":"Soybeans/Oats "},{"value":241,"color_hint":"FFD200","description":"Corn/Soybeans"},{"value":242,"color_hint":"000099","description":"Blueberries"},{"value":243,"color_hint":"FF6666","description":"Cabbage"},{"value":244,"color_hint":"FF6666","description":"Cauliflower"},{"value":245,"color_hint":"FF6666","description":"Celery"},{"value":246,"color_hint":"FF6666","description":"Radishes"},{"value":247,"color_hint":"FF6666","description":"Turnips"},{"value":248,"color_hint":"FF6666","description":"Eggplants"},{"value":249,"color_hint":"FF6666","description":"Gourds"},{"value":250,"color_hint":"FF6666","description":"Cranberries"},{"value":254,"color_hint":"256F00","description":"Barley/Soybeans"}]},"soybeans":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":255,"data_type":"uint8","spatial_resolution":30}]},"confidence":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":0,"data_type":"uint8","spatial_resolution":30}]},"cultivated":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":0,"data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"value":1,"color_hint":"000000","description":"Non-Cultivated"},{"value":2,"color_hint":"006300","description":"Cultivated"}]}},"stac_version":"1.0.0","msft:container":"usda-cdl","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/classification/v1.1.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"The UDA Cropland Data Layer is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground truth collected during the current growing season.","msft:region":"westeurope"},{"id":"eclipse","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse"},{"rel":"license","href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Microsoft%20Project%20Eclipse%20API%20Terms%20of%20Use_Mar%202022.pdf","type":"application/pdf","title":"Terms of use"},{"rel":"cite-as","href":"https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf","type":"application/pdf","title":"Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environment Sensing in Cities"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/eclipse","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Urban Innovation Eclipse Sensor Data","assets":{"data":{"href":"abfs://eclipse/Chicago/","type":"application/x-parquet","roles":["data"],"title":"Full dataset","description":"Full parquet dataset","table:storage_options":{"account_name":"ai4edataeuwest"}},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/eclipse-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"Urban Innovation Chicago Sensors"},"geoparquet-items":{"href":"abfs://items/eclipse.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-87.94011408252348,41.64454312178303,-87.5241371038952,42.023038586147585]]},"temporal":{"interval":[["2021-01-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["Eclipse","PM25","air pollution"],"providers":[{"url":"https://www.microsoft.com/en-us/research/urban-innovation-research/","name":"Urban Innovation","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Weekly dataset","table:storage_options":{"account_name":"ai4edataeuwest"}}},"sci:citation":"Daepp, Cabral, Ranganathan et al. (2022) Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy. Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities","stac_version":"1.0.0","table:columns":[{"name":"City","type":"byte_array","description":"City where the Microsoft Eclipse device is deployed"},{"name":"DeviceId","type":"int32","description":"Id for a given device"},{"name":"LocationName","type":"byte_array","description":"Unique string describing the device location"},{"name":"Latitude","type":"double","description":"Latitude of the device location"},{"name":"Longitude","type":"double","description":"Longitude of the device location"},{"name":"ReadingDateTimeUTC","type":"int96","description":"The UTC date time string (like 2022-03-04 20:27:25.000) when the reading from the Eclipse sensor was recorded"},{"name":"PM25","type":"double","description":"Uncalibrated Fine particulate matter (PM 2.5) in µg/m³"},{"name":"CalibratedPM25","type":"double","description":"Calibrated PM 2.5 in µg/m³"},{"name":"CalibratedO3","type":"double","description":"Calibrated Ozone in PPB"},{"name":"CalibratedNO2","type":"double","description":"Calibrated Nitrogen Dioxide in PPB"},{"name":"CO","type":"double","description":"Uncalibrated Carbon monoxide (CO) in PPM"},{"name":"Temperature","type":"double","description":"Degree Celsius"},{"name":"Humidity","type":"double","description":"Relative humidity"},{"name":"BatteryLevel","type":"double","description":"Device battery level in Volts"},{"name":"PercentBattery","type":"double","description":"Percent of device battery"},{"name":"CellSignal","type":"double","description":"Cellular signal strength in dB"}],"msft:container":"eclipse","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"A network of low-cost air quality sensing network for cities and led by the Urban Innovation Group at Microsoft Research","msft:region":"westeurope"},{"id":"esa-cci-lc","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc"},{"rel":"license","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/satellite-land-cover.pdf","type":"text/html","title":"ESA CCI license"},{"rel":"license","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf","type":"text/html","title":"COPERNICUS 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The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n","item_assets":{"lccs_class":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Land Cover Class Defined in the Land Cover Classification System","description":"Land cover class per pixel, defined using the Land Cover Classification System developed by the United Nations Food and Agriculture Organization.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":300}],"classification:classes":[{"name":"no-data","value":0,"no_data":true,"color_hint":"000000","description":"No 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open (>15%)"},{"name":"tree-2a","value":61,"regional":true,"color_hint":"00A000","description":"Tree cover, broadleaved, deciduous, closed (>40%)"},{"name":"tree-2b","value":62,"regional":true,"color_hint":"AAC800","description":"Tree cover, broadleaved, deciduous, open (15-40%)"},{"name":"tree-3","value":70,"color_hint":"003C00","description":"Tree cover, needleleaved, evergreen, closed to open (>15%)"},{"name":"tree-3a","value":71,"regional":true,"color_hint":"003C00","description":"Tree cover, needleleaved, evergreen, closed (>40%)"},{"name":"tree-3b","value":72,"regional":true,"color_hint":"005000","description":"Tree cover, needleleaved, evergreen, open (15-40%)"},{"name":"tree-4","value":80,"color_hint":"285000","description":"Tree cover, needleleaved, deciduous, closed to open (>15%)"},{"name":"tree-4a","value":81,"regional":true,"color_hint":"285000","description":"Tree cover, needleleaved, deciduous, closed (>40%)"},{"name":"tree-4b","value":82,"regional":true,"color_hint":"286400","description":"Tree cover, needleleaved, deciduous, open (15-40%)"},{"name":"tree-5","value":90,"color_hint":"788200","description":"Tree cover, mixed leaf type (broadleaved and needleleaved)"},{"name":"tree-shrub","value":100,"color_hint":"8CA000","description":"Mosaic tree and shrub (>50%) / herbaceous cover (<50%)"},{"name":"herbaceous","value":110,"color_hint":"BE9600","description":"Mosaic herbaceous cover (>50%) / tree and shrub (<50%)"},{"name":"shrubland","value":120,"color_hint":"966400","description":"Shrubland"},{"name":"shrubland-a","value":121,"regional":true,"color_hint":"966400","description":"Evergreen shrubland"},{"name":"shrubland-b","value":122,"regional":true,"color_hint":"966400","description":"Deciduous shrubland"},{"name":"grassland","value":130,"color_hint":"FFB432","description":"Grassland"},{"name":"lichens-moses","value":140,"color_hint":"FFDCD2","description":"Lichens and mosses"},{"name":"sparse-veg","value":150,"color_hint":"FFEBAF","description":"Sparse vegetation (tree, shrub, herbaceous cover) (<15%)"},{"name":"sparse-veg-a","value":151,"regional":true,"color_hint":"FFC864","description":"Sparse tree (<15%)"},{"name":"sparse-veg-b","value":152,"regional":true,"color_hint":"FFD278","description":"Sparse shrub (<15%)"},{"name":"sparse-veg-c","value":153,"regional":true,"color_hint":"FFEBAF","description":"Sparse herbaceous cover (<15%)"},{"name":"flooded-tree-1","value":160,"color_hint":"00785A","description":"Tree cover, flooded, fresh or brackish water"},{"name":"flooded-tree-2","value":170,"color_hint":"009678","description":"Tree cover, flooded, saline water"},{"name":"flooded-shrub-herbaceous","value":180,"color_hint":"00DC82","description":"Shrub or herbaceous cover, flooded, fresh/saline/brackish water"},{"name":"urban","value":190,"color_hint":"C31400","description":"Urban areas"},{"name":"bare","value":200,"color_hint":"FFF5D7","description":"Bare areas"},{"name":"bare-a","value":201,"regional":true,"color_hint":"DCDCDC","description":"Consolidated bare areas"},{"name":"bare-b","value":202,"regional":true,"color_hint":"FFF5D7","description":"Unconsolidated bare areas"},{"name":"water","value":210,"color_hint":"0046C8","description":"Water bodies"},{"name":"snow-ice","value":220,"color_hint":"FFFFFF","description":"Permanent snow and ice"}]},"change_count":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["quality"],"title":"Number of Class Changes","description":"Number of years where land cover class changes have occurred, since 1992. 0 for stable, greater than 0 for changes.","raster:bands":[{"sampling":"area","data_type":"uint8","spatial_resolution":300}]},"processed_flag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["quality"],"title":"Land Cover Map Processed Area Flag","description":"Flag to mark areas that could not be classified.","raster:bands":[{"nodata":255,"sampling":"area","data_type":"uint8","spatial_resolution":300}],"classification:classes":[{"name":"not_processed","value":0,"description":"Not processed"},{"name":"processed","value":1,"description":"Processed"}]},"observation_count":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["quality"],"title":"Number of Valid Observations","description":"Number of valid satellite observations that have contributed to each pixel's classification.","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":300}]},"current_pixel_state":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["quality"],"title":"Land Cover Pixel Type Mask","description":"Pixel identification from satellite surface reflectance observations, mainly distinguishing between land, water, and snow/ice.","raster:bands":[{"nodata":255,"sampling":"area","data_type":"uint8","spatial_resolution":300}],"classification:classes":[{"name":"land","value":1,"description":"Clear land"},{"name":"water","value":2,"description":"Clear water"},{"name":"snow","value":3,"description":"Clear snow / ice"},{"name":"cloud","value":4,"description":"Cloud"},{"name":"cloud_shadow","value":5,"description":"Cloud shadow"},{"name":"filled","value":6,"description":"Filled"}]}},"stac_version":"1.0.0","msft:group_id":"esa-cci-lc","msft:container":"esa-cci-lc","stac_extensions":["https://stac-extensions.github.io/classification/v1.1.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"Tiled ESA CCI global land cover maps in COG format","msft:region":"westeurope"},{"id":"esa-cci-lc-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc-netcdf"},{"rel":"license","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/satellite-land-cover.pdf","type":"text/html","title":"ESA CCI license"},{"rel":"license","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf","type":"text/html","title":"COPERNICUS license"},{"rel":"license","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/vito-proba-v.pdf","type":"text/html","title":"VITO License"},{"rel":"about","href":"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover","type":"text/html","title":"Product Landing Page"},{"rel":"about","href":"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf","type":"application/pdf","title":"Product user guide for version 2.1"},{"rel":"about","href":"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf","type":"application/pdf","title":"Product user guide for version 2.0"},{"rel":"cite-as","href":"https://doi.org/10.24381/cds.006f2c9a"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-cci-lc-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ESA Climate Change Initiative Land Cover Maps (NetCDF)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/esa-cci-lc-netcdf-thumb.png","type":"image/png","roles":["thumbnail"],"title":"ESA CCI Land Cover NetCDF Thumbnail"},"geoparquet-items":{"href":"abfs://items/esa-cci-lc-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-90.0,180.0,90.0]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"proprietary","sci:doi":"10.24381/cds.006f2c9a","keywords":["Land Cover","ESA","CCI","Global"],"providers":[{"url":"https://vito.be","name":"VITO","roles":["licensor"],"description":"Provides the PROBA-V source data (for v2.0)."},{"url":"https://uclouvain.be","name":"UCLouvain","roles":["producer"],"description":"UCLouvain produces the dataset (v2.1) for the ESA Climate Change Initiative."},{"url":"https://brockmann-consult.de","name":"Brockmann Consult","roles":["processor"],"description":"Brockmann Consult is responsible for the required pre-processing and the distribution of the dataset (v2.1)."},{"url":"http://esa-landcover-cci.org","name":"ESA Climate Change Initiative","roles":["licensor"],"description":"The ESA Climate Change Initiative (CCI) is leading the product creation."},{"url":"https://copernicus.eu","name":"Copernicus","roles":["licensor"],"description":"Hosts the data on the Copernicus Climate Data Store (CDS)."},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"esa_cci_lc:version":["2.0.7cds","2.1.1"]},"description":"The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.","item_assets":{"netcdf":{"type":"application/netcdf","roles":["data","quality"],"title":"ESA CCI Land Cover NetCDF 4 File"}},"stac_version":"1.0.0","msft:group_id":"esa-cci-lc","msft:container":"esa-cci-lc","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"landcoverdata","msft:short_description":"ESA CCI global land cover maps in NetCDF format","msft:region":"westeurope"},{"id":"fws-nwi","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi"},{"rel":"describedby","href":"https://www.fws.gov/wetlands/Data/metadata/FWS_Wetlands.xml","type":"application/xml","title":"Wetlands metadata"},{"rel":"about","href":"https://www.fws.gov/sites/default/files/documents/national-wetlands-inventory-fact-sheet.pdf","type":"application/pdf","title":"Project Fact Sheet"},{"rel":"about","href":"https://www.fws.gov/program/national-wetlands-inventory","type":"text/html","title":"Project Landing Page"},{"rel":"license","href":"http://www.usa.gov/publicdomain/label/1.0/","type":"text/html","title":"US Public Domain"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/fws-nwi","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"FWS National Wetlands Inventory","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fws-nwi-thumb.png","type":"image/png","roles":["thumbnail"],"title":"FWS National Wetlands Inventory thumbnail"}},"extent":{"spatial":{"bbox":[[-64.54958,13.16667,144.6,71.99633],[144.6,13.16667,180.0,71.99633],[-180.0,13.16667,-64.54958,71.99633]]},"temporal":{"interval":[["2022-10-01T00:00:00Z","2022-10-01T00:00:00Z"]]}},"license":"proprietary","keywords":["USFWS","Wetlands","United States"],"providers":[{"url":"https://www.fws.gov","name":"U.S. Fish and Wildlife Service","email":"wetlands_team@fws.gov","roles":["producer","licensor"],"description":"The U.S. Fish and Wildlife Service is the principal federal agency tasked with providing information to the public on the extent and status of the nation's wetland and deepwater habitats, as well as changes to these habitats over time."},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"fws_nwi:state":["Alabama","Alaska","Arizona","Arkansas","California","Colorado","Connecticut","Delaware","District of Columbia","Florida","Georgia","Hawaii","Idaho","Illinois","Indiana","Iowa","Kansas","Kentucky","Louisiana","Maine","Maryland","Massachusetts","Michigan","Minnesota","Mississippi","Missouri","Montana","Nebraska","Nevada","New Hampshire","New Jersey","New Mexico","New York","North Carolina","North Dakota","Ohio","Oklahoma","Oregon","Pacific Trust Islands","Pennsylvania","Puerto Rico and Virgin Islands","Rhode Island","South Carolina","South Dakota","Tennessee","Texas","Utah","Vermont","Virginia","Washington","West Virginia","Wisconsin","Wyoming"],"fws_nwi:content":["riparian","historic_wetlands","wetlands"],"fws_nwi:state_code":["AL","AK","AZ","AR","CA","CO","CT","DE","DC","FL","GA","HI","ID","IL","IN","IA","KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ","NM","NY","NC","ND","OH","OK","OR","PacTrust","PA","PRVI","RI","SC","SD","TN","TX","UT","VT","VA","WA","WV","WI","WY"]},"description":"The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\n**NOTE:** Due to the variation in use and analysis of this data by the end user, each  state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.","item_assets":{"zip":{"type":"application/zip","roles":["data","archive","source"]}},"stac_version":"1.0.0","msft:container":"fws-nwi","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Vector dataset containing wetlands boundaries and identification across the United States.","msft:region":"westeurope"},{"id":"usgs-lcmap-conus-v13","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13"},{"rel":"about","href":"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products","type":"text/html","title":"LCMAP CONUS Science Products"},{"rel":"license","href":"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products","type":"text/html","title":"Proprietary, Unrestricted"},{"rel":"source","href":"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access","type":"text/html","title":"USGS Data Access Options"},{"rel":"cite-as","href":"https://doi.org/10.1016/j.rse.2019.111356"},{"rel":"cite-as","href":"https://doi.org/10.1016/j.rse.2014.01.011"},{"rel":"cite-as","href":"https://doi.org/10.5066/P9C46NG0"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/usgs-lcmap-conus-v13","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"USGS LCMAP CONUS Collection 1.3","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usgs-lcmap-conus-v13-thumb.png","type":"image/png","roles":["thumbnail"],"title":"USGS LCMAP CONUS 1.3 Thumbnail"},"geoparquet-items":{"href":"abfs://items/usgs-lcmap-conus-v13.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-129.27732,21.805095,-63.11843,52.92172]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2021-12-31T00:00:00Z"]]}},"license":"proprietary","sci:doi":"10.5066/P9C46NG0","keywords":["USGS","LCMAP","Land Cover","Land Cover Change","CONUS"],"providers":[{"url":"https://www.usgs.gov/special-topics/lcmap","name":"United States Geological Survey","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"usgs_lcmap:vertical_tile":{"maximum":20,"minimum":0},"usgs_lcmap:horizontal_tile":{"maximum":32,"minimum":1}},"description":"The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n","item_assets":{"dates":{"type":"text/plain","roles":["metadata"],"title":"Landsat Observation Dates","description":"Landsat observation dates used as input to the CCDC algorithm."},"lcpri":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Primary Land Cover","description":"Land cover classification consisting of eight general land cover types. The most likely land cover according to the modeling process.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"nodata","value":0,"color_hint":"000000","description":"No Data"},{"name":"developed","value":1,"color_hint":"ff3232","description":"Developed"},{"name":"cropland","value":2,"color_hint":"be8c5a","description":"Cropland"},{"name":"grass_shrub","value":3,"color_hint":"e6f0d2","description":"Grass/Shrub"},{"name":"tree_cover","value":4,"color_hint":"1c6330","description":"Tree Cover"},{"name":"water","value":5,"color_hint":"0070ff","description":"Water"},{"name":"wetlands","value":6,"color_hint":"b3d9ff","description":"Wetlands"},{"name":"ice_snow","value":7,"color_hint":"ffffff","description":"Snow/Ice"},{"name":"barren","value":8,"color_hint":"b3aea3","description":"Barren"}]},"lcsec":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Secondary Land Cover","description":"Land cover classification consisting of eight general land cover types. The second most likely land cover according to the modeling process.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"nodata","value":0,"color_hint":"000000","description":"No Data"},{"name":"developed","value":1,"color_hint":"ff3232","description":"Developed"},{"name":"cropland","value":2,"color_hint":"be8c5a","description":"Cropland"},{"name":"grass_shrub","value":3,"color_hint":"e6f0d2","description":"Grass/Shrub"},{"name":"tree_cover","value":4,"color_hint":"1c6330","description":"Tree Cover"},{"name":"water","value":5,"color_hint":"0070ff","description":"Water"},{"name":"wetlands","value":6,"color_hint":"b3d9ff","description":"Wetlands"},{"name":"ice_snow","value":7,"color_hint":"ffffff","description":"Snow/Ice"},{"name":"barren","value":8,"color_hint":"b3aea3","description":"Barren"}]},"scmag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Change Magnitude","description":"The spectral strength or intensity of a time series model 'break' when spectral observations have diverged from the model predictions.","raster:bands":[{"sampling":"area","data_type":"float32","spatial_resolution":30}]},"scmqa":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Model Quality","description":"Information regarding the type of time series model applied to the current product year.","raster:bands":[{"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"no_model","value":0,"description":"No model established for July 1st of current year."},{"name":"simple_model","value":4,"description":"A partial, 4-coefficient harmonic model."},{"name":"advanced_model","value":6,"description":"A partial, 6-coefficient harmonic model."},{"name":"full_model","value":8,"description":"A full, 8-coefficient harmonic model."},{"name":"start_fit","value":14,"description":"A simple model at the beginning of a time series where sparse and/or highly variable spectral measurements prevent establishment of a harmonic model."},{"name":"end_fit","value":24,"description":"A simple model at the end of a time series where there are insufficient observations and/or time to establish a new harmonic model following a model break."},{"name":"insufficient_clear","value":44,"description":"A simple model for the entire time series in cases where fewer than 25% of input observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel quality band (PIXELQA)."},{"name":"persistent_snow","value":54,"description":"A simple model for the entire time series in cases where 75% or more of input observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality band (PIXELQA)."}]},"browse":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"USGS Browse Image","description":"Image generated by USGS for viewing LCMAP 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Barren"},{"name":"7_to_1","value":71,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Developed"},{"name":"7_to_2","value":72,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Cropland"},{"name":"7_to_3","value":73,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Grass/Shrub"},{"name":"7_to_4","value":74,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Tree Cover"},{"name":"7_to_5","value":75,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Water"},{"name":"7_to_6","value":76,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Wetlands"},{"name":"7_to_8","value":78,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Barren"},{"name":"8_to_1","value":81,"color_hint":"ab00d6","description":"Cover change from Barren to Developed"},{"name":"8_to_2","value":82,"color_hint":"ab00d6","description":"Cover change from Barren to Cropland"},{"name":"8_to_3","value":83,"color_hint":"ab00d6","description":"Cover change from Barren to Grass/Shrub"},{"name":"8_to_4","value":84,"color_hint":"ab00d6","description":"Cover change from Barren to Tree Cover"},{"name":"8_to_5","value":85,"color_hint":"ab00d6","description":"Cover change from Barren to Water"},{"name":"8_to_6","value":86,"color_hint":"ab00d6","description":"Cover change from Barren to Wetlands"},{"name":"8_to_7","value":87,"color_hint":"ab00d6","description":"Cover change from Barren to Snow/Ice"}]},"sclast":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Time Since Last Change","description":"Time, in days, since the last identified Spectral Change (SCTIME).","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"scstab":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Spectral Stability Period","description":"Measure of the amount of time 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Current spectral state can refer to both during stable time series segments or a period outside of stable time series segments.","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"sctime":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Time of Spectral Change","description":"Represents the timing of a spectral change within the current product year as the day of year the change occurred.","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"lcpconf":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Primary Land Cover Confidence","description":"Provides provenance tracking and a measure of confidence that the Primary Land Cover label matches the training data.","file:values":[{"values":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"summary":"Measure of confidence that the Primary Land Cover label matches the training data."},{"values":[151],"summary":"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class. Primary Land Cover class assignment based on secondary analysis."},{"values":[152],"summary":"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class. Primary Land cover class assignment based on secondary analysis."},{"values":[201],"summary":"No stable time series models were produced for this location. Primary Land Cover was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP Level 1 classification schema, see LCMAP Science Product Guide for more information)."},{"values":[202],"summary":"Insufficient data available to extend most recent time series model past July 1st of current year. Land cover assigned the last identified cover class from earlier year."},{"values":[211],"summary":"July 1st falls in a gap between two stable time series models of the same land cover class. Primary Land Cover assigned the primary land cover class of those before/after models."},{"values":[212],"summary":"July 1st falls in a gap between two stable time series models of differing land cover class. If July 1st is before the 'break date' of the earlier model, Primary Land Cover is assigned the primary land cover class of that earlier model. Otherwise, Primary Land Cover is assigned the primary land cover class of the subsequent, later model."},{"values":[213],"summary":"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Primary Land Cover assigned the primary land cover class of 1st subsequent model."},{"values":[214],"summary":"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Primary Land Cover assigned the last identified primary land cover class from earlier year."}],"raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]},"lcsconf":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Secondary Land Cover Confidence","description":"Provides provenance tracking and a measure of confidence that the Secondary Land Cover label matches the training data.","file:values":[{"values":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"summary":"Measure of confidence that the Secondary Land Cover label matches the training data."},{"values":[151],"summary":"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary."},{"values":[152],"summary":"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary."},{"values":[201],"summary":"No stable time series models were produced for this location. Secondary Land Cover assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP classification schema, see LCMAP Science Product Guide for more information)."},{"values":[202],"summary":"Insufficient data available to extend most recent time series model past July 1st of current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year."},{"values":[211],"summary":"July 1st falls in a gap between two stable time series models of the same secondary land cover class. Secondary Land Cover assigned the land cover class of those before/after models."},{"values":[212],"summary":"July 1st falls in a gap between two stable time series models of differing secondary land cover classes. If July 1st is before the 'break date' of the earlier model, Secondary Land Cover is assigned the secondary land cover class of the earlier model. Otherwise, Secondary Land Cover is assigned the secondary land cover class of the subsequent model."},{"values":[213],"summary":"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Secondary Land Cover assigned the secondary land cover class of 1st subsequent model."},{"values":[214],"summary":"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year."}],"raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]},"lcpri_metadata":{"type":"application/xml","roles":["metadata"],"title":"Primary Land Cover Metadata","description":"Primary Land Cover product metadata"},"lcsec_metadata":{"type":"application/xml","roles":["metadata"],"title":"Secondary Land Cover Metadata","description":"Secondary Land Cover product metadata"},"scmag_metadata":{"type":"application/xml","roles":["metadata"],"title":"Change Magnitude Metadata","description":"Change Magnitude product metadata"},"scmqa_metadata":{"type":"application/xml","roles":["metadata"],"title":"Model Quality Metadata","description":"Model Quality product metadata"},"lcachg_metadata":{"type":"application/xml","roles":["metadata"],"title":"Annual Land Cover Change Metadata","description":"Annual Land Cover Change product 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and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356"},{"doi":"10.1016/j.rse.2014.01.011","citation":"Zhu, Z., and Woodcock, C.E., 2014, Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing of Environment, v. 144, p. 152-171"}],"msft:storage_account":"landcoverdata","msft:short_description":"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3 Science Products for the Conterminous United States.","msft:region":"westeurope"},{"id":"usgs-lcmap-hawaii-v10","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10"},{"rel":"about","href":"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products","type":"text/html","title":"LCMAP Hawaii Science Products"},{"rel":"license","href":"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products","type":"text/html","title":"Proprietary, 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Survey","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"usgs_lcmap:vertical_tile":{"maximum":2,"minimum":0},"usgs_lcmap:horizontal_tile":{"maximum":4,"minimum":0}},"description":"The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n","item_assets":{"dates":{"type":"text/plain","roles":["metadata"],"title":"Landsat Observation Dates","description":"Landsat observation dates used as input to the CCDC algorithm."},"lcpri":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Primary Land Cover","description":"Land cover classification consisting of eight general land cover types. The most likely land cover according to the modeling process.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"nodata","value":0,"color_hint":"000000","description":"No Data"},{"name":"developed","value":1,"color_hint":"ff3232","description":"Developed"},{"name":"cropland","value":2,"color_hint":"be8c5a","description":"Cropland"},{"name":"grass_shrub","value":3,"color_hint":"e6f0d2","description":"Grass/Shrub"},{"name":"tree_cover","value":4,"color_hint":"1c6330","description":"Tree Cover"},{"name":"water","value":5,"color_hint":"0070ff","description":"Water"},{"name":"wetlands","value":6,"color_hint":"b3d9ff","description":"Wetlands"},{"name":"ice_snow","value":7,"color_hint":"ffffff","description":"Snow/Ice"},{"name":"barren","value":8,"color_hint":"b3aea3","description":"Barren"}]},"lcsec":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Secondary Land Cover","description":"Land cover classification consisting of eight general land cover types. The second most likely land cover according to the modeling process.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"nodata","value":0,"color_hint":"000000","description":"No Data"},{"name":"developed","value":1,"color_hint":"ff3232","description":"Developed"},{"name":"cropland","value":2,"color_hint":"be8c5a","description":"Cropland"},{"name":"grass_shrub","value":3,"color_hint":"e6f0d2","description":"Grass/Shrub"},{"name":"tree_cover","value":4,"color_hint":"1c6330","description":"Tree Cover"},{"name":"water","value":5,"color_hint":"0070ff","description":"Water"},{"name":"wetlands","value":6,"color_hint":"b3d9ff","description":"Wetlands"},{"name":"ice_snow","value":7,"color_hint":"ffffff","description":"Snow/Ice"},{"name":"barren","value":8,"color_hint":"b3aea3","description":"Barren"}]},"scmag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Change 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model at the beginning of a time series where sparse and/or highly variable spectral measurements prevent establishment of a harmonic model."},{"name":"end_fit","value":24,"description":"A simple model at the end of a time series where there are insufficient observations and/or time to establish a new harmonic model following a model break."},{"name":"insufficient_clear","value":44,"description":"A simple model for the entire time series in cases where fewer than 25% of input observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel quality band (PIXELQA)."},{"name":"persistent_snow","value":54,"description":"A simple model for the entire time series in cases where 75% or more of input observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality band (PIXELQA)."}]},"browse":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"USGS Browse Image","description":"Image generated by USGS for viewing LCMAP classification data in web applications.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]},"lcachg":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Annual Land Cover Change","description":"Synthesis of Primary Land Cover of current and previous year identifying changes in land cover class.","raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}],"classification:classes":[{"name":"nodata","value":0,"color_hint":"000000","description":"No Data"},{"name":"developed","value":1,"color_hint":"ff3232","description":"Developed"},{"name":"cropland","value":2,"color_hint":"be8c5a","description":"Cropland"},{"name":"grass_shrub","value":3,"color_hint":"e6f0d2","description":"Grass/Shrub"},{"name":"tree_cover","value":4,"color_hint":"1c6330","description":"Tree Cover"},{"name":"water","value":5,"color_hint":"0070ff","description":"Water"},{"name":"wetlands","value":6,"color_hint":"b3d9ff","description":"Wetlands"},{"name":"ice_snow","value":7,"color_hint":"ffffff","description":"Snow/Ice"},{"name":"barren","value":8,"color_hint":"b3aea3","description":"Barren"},{"name":"1_to_2","value":12,"color_hint":"ab00d6","description":"Cover change from Developed to Cropland"},{"name":"1_to_3","value":13,"color_hint":"ab00d6","description":"Cover change from Developed to Grass/Shrub"},{"name":"1_to_4","value":14,"color_hint":"ab00d6","description":"Cover change from Developed to Tree Cover"},{"name":"1_to_5","value":15,"color_hint":"ab00d6","description":"Cover change from Developed to Water"},{"name":"1_to_6","value":16,"color_hint":"ab00d6","description":"Cover change from Developed to Wetlands"},{"name":"1_to_7","value":17,"color_hint":"ab00d6","description":"Cover change from Developed to 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Developed"},{"name":"3_to_2","value":32,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Cropland"},{"name":"3_to_4","value":34,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Tree Cover"},{"name":"3_to_5","value":35,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Water"},{"name":"3_to_6","value":36,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Wetlands"},{"name":"3_to_7","value":37,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Snow/Ice"},{"name":"3_to_8","value":38,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to Barren"},{"name":"4_to_1","value":41,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Developed"},{"name":"4_to_2","value":42,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Cropland"},{"name":"4_to_3","value":43,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Grass/Shrub"},{"name":"4_to_5","value":45,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Water"},{"name":"4_to_6","value":46,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Wetlands"},{"name":"4_to_7","value":47,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Snow/Ice"},{"name":"4_to_8","value":48,"color_hint":"ab00d6","description":"Cover change from Tree Cover to Barren"},{"name":"5_to_1","value":51,"color_hint":"ab00d6","description":"Cover change from Water to Developed"},{"name":"5_to_2","value":52,"color_hint":"ab00d6","description":"Cover change from Water to Cropland"},{"name":"5_to_3","value":53,"color_hint":"ab00d6","description":"Cover change from Water to Grass/Shrub"},{"name":"5_to_4","value":54,"color_hint":"ab00d6","description":"Cover change from Water to Tree Cover"},{"name":"5_to_6","value":56,"color_hint":"ab00d6","description":"Cover change from Water to Wetlands"},{"name":"5_to_7","value":57,"color_hint":"ab00d6","description":"Cover change from Water to Snow/Ice"},{"name":"5_to_8","value":58,"color_hint":"ab00d6","description":"Cover change from Water to Barren"},{"name":"6_to_1","value":61,"color_hint":"ab00d6","description":"Cover change from Wetlands to Developed"},{"name":"6_to_2","value":62,"color_hint":"ab00d6","description":"Cover change from Wetlands to Cropland"},{"name":"6_to_3","value":63,"color_hint":"ab00d6","description":"Cover change from Wetlands to Grass/Shrub"},{"name":"6_to_4","value":64,"color_hint":"ab00d6","description":"Cover change from Wetlands to Tree Cover"},{"name":"6_to_5","value":65,"color_hint":"ab00d6","description":"Cover change from Wetlands to Water"},{"name":"6_to_7","value":67,"color_hint":"ab00d6","description":"Cover change from Wetlands to Snow/Ice"},{"name":"6_to_8","value":68,"color_hint":"ab00d6","description":"Cover change from Wetlands to Barren"},{"name":"7_to_1","value":71,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Developed"},{"name":"7_to_2","value":72,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Cropland"},{"name":"7_to_3","value":73,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Grass/Shrub"},{"name":"7_to_4","value":74,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Tree Cover"},{"name":"7_to_5","value":75,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Water"},{"name":"7_to_6","value":76,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Wetlands"},{"name":"7_to_8","value":78,"color_hint":"ab00d6","description":"Cover change from Snow/Ice to Barren"},{"name":"8_to_1","value":81,"color_hint":"ab00d6","description":"Cover change from Barren to Developed"},{"name":"8_to_2","value":82,"color_hint":"ab00d6","description":"Cover change from Barren to Cropland"},{"name":"8_to_3","value":83,"color_hint":"ab00d6","description":"Cover change from Barren to Grass/Shrub"},{"name":"8_to_4","value":84,"color_hint":"ab00d6","description":"Cover change from Barren to Tree Cover"},{"name":"8_to_5","value":85,"color_hint":"ab00d6","description":"Cover change from Barren to Water"},{"name":"8_to_6","value":86,"color_hint":"ab00d6","description":"Cover change from Barren to Wetlands"},{"name":"8_to_7","value":87,"color_hint":"ab00d6","description":"Cover change from Barren to Snow/Ice"}]},"sclast":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Time Since Last Change","description":"Time, in days, since the last identified Spectral Change (SCTIME).","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"scstab":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Spectral Stability Period","description":"Measure of the amount of time in days that a pixel has been in its current spectral state as of July 1st. Current spectral state can refer to both during stable time series segments or a period outside of stable time series segments.","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"sctime":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Time of Spectral Change","description":"Represents the timing of a spectral change within the current product year as the day of year the change occurred.","raster:bands":[{"sampling":"area","data_type":"uint16","spatial_resolution":30}]},"lcpconf":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Primary Land Cover Confidence","description":"Provides provenance tracking and a measure of confidence that the Primary Land Cover label matches the training data.","file:values":[{"values":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"summary":"Measure of confidence that the Primary Land Cover label matches the training data."},{"values":[151],"summary":"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class. Primary Land Cover class assignment based on secondary analysis."},{"values":[152],"summary":"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class. Primary Land cover class assignment based on secondary analysis."},{"values":[201],"summary":"No stable time series models were produced for this location. Primary Land Cover was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP Level 1 classification schema, see LCMAP Science Product Guide for more information)."},{"values":[202],"summary":"Insufficient data available to extend most recent time series model past July 1st of current year. Land cover assigned the last identified cover class from earlier year."},{"values":[211],"summary":"July 1st falls in a gap between two stable time series models of the same land cover class. Primary Land Cover assigned the primary land cover class of those before/after models."},{"values":[212],"summary":"July 1st falls in a gap between two stable time series models of differing land cover class. If July 1st is before the 'break date' of the earlier model, Primary Land Cover is assigned the primary land cover class of that earlier model. Otherwise, Primary Land Cover is assigned the primary land cover class of the subsequent, later model."},{"values":[213],"summary":"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Primary Land Cover assigned the primary land cover class of 1st subsequent model."},{"values":[214],"summary":"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Primary Land Cover assigned the last identified primary land cover class from earlier year."}],"raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]},"lcsconf":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Secondary Land Cover Confidence","description":"Provides provenance tracking and a measure of confidence that the Secondary Land Cover label matches the training data.","file:values":[{"values":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"summary":"Measure of confidence that the Secondary Land Cover label matches the training data."},{"values":[151],"summary":"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary."},{"values":[152],"summary":"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary."},{"values":[201],"summary":"No stable time series models were produced for this location. Secondary Land Cover assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP classification schema, see LCMAP Science Product Guide for more information)."},{"values":[202],"summary":"Insufficient data available to extend most recent time series model past July 1st of current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year."},{"values":[211],"summary":"July 1st falls in a gap between two stable time series models of the same secondary land cover class. Secondary Land Cover assigned the land cover class of those before/after models."},{"values":[212],"summary":"July 1st falls in a gap between two stable time series models of differing secondary land cover classes. If July 1st is before the 'break date' of the earlier model, Secondary Land Cover is assigned the secondary land cover class of the earlier model. Otherwise, Secondary Land Cover is assigned the secondary land cover class of the subsequent model."},{"values":[213],"summary":"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Secondary Land Cover assigned the secondary land cover class of 1st subsequent model."},{"values":[214],"summary":"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year."}],"raster:bands":[{"nodata":0,"sampling":"area","data_type":"uint8","spatial_resolution":30}]},"lcpri_metadata":{"type":"application/xml","roles":["metadata"],"title":"Primary Land Cover Metadata","description":"Primary Land Cover product metadata"},"lcsec_metadata":{"type":"application/xml","roles":["metadata"],"title":"Secondary Land Cover Metadata","description":"Secondary Land Cover product metadata"},"scmag_metadata":{"type":"application/xml","roles":["metadata"],"title":"Change Magnitude Metadata","description":"Change Magnitude product metadata"},"scmqa_metadata":{"type":"application/xml","roles":["metadata"],"title":"Model Quality Metadata","description":"Model Quality product metadata"},"lcachg_metadata":{"type":"application/xml","roles":["metadata"],"title":"Annual Land Cover Change Metadata","description":"Annual Land Cover Change product metadata"},"sclast_metadata":{"type":"application/xml","roles":["metadata"],"title":"Time Since Last Change Metadata","description":"Time Since Last Change product metadata"},"scstab_metadata":{"type":"application/xml","roles":["metadata"],"title":"Spectral Stability Period Metadata","description":"Spectral Stability Period product metadata"},"sctime_metadata":{"type":"application/xml","roles":["metadata"],"title":"Time of Spectral Change Metadata","description":"Time of Spectral Change product metadata"},"lcpconf_metadata":{"type":"application/xml","roles":["metadata"],"title":"Primary Land Cover Confidence Metadata","description":"Primary Land Cover Confidence product metadata"},"lcsconf_metadata":{"type":"application/xml","roles":["metadata"],"title":"Secondary Land Cover Confidence Metadata","description":"Secondary Land Cover Confidence product metadata"}},"sci:citation":"U.S. Geological Survey (USGS), 2022, Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products for Hawaii: USGS data release","stac_version":"1.0.0","msft:group_id":"usgs-lcmap","msft:container":"lcmap","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/classification/v1.1.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json"],"sci:publications":[{"doi":"10.1016/j.jag.2022.103015","citation":"Li, C., Xian, G., Wellington, D., Smith, K., Horton, J., &amp; Zhou, Q., 2022, Development of the LCMAP annual land cover product across Hawaii: International Journal of Applied Earth Observation and Geoinformation, v. 113, article 103015."},{"doi":"10.1016/j.rse.2019.111356","citation":"Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing an operational continuous United States national land change monitoring capability-The Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356"}],"msft:storage_account":"landcoverdata","msft:short_description":"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products for Hawaii","msft:region":"westeurope"},{"id":"noaa-climate-normals-tabular","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-tabular/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-tabular"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS-D-11-00197.1"},{"rel":"cite-as","href":"https://doi.org/10.1175/BAMS-D-11-00173.1"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals","type":"text/html","title":"NOAA U.S. Climate Normals Landing Page"},{"rel":"license","href":"https://www.noaa.gov/information-technology/open-data-dissemination","type":"text/html","title":"NOAA Open Data Dissemination"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-tabular/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-tabular","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA US Tabular Climate Normals","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-tabular-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA U.S. Tabular Climate Normals Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-climate-normals-tabular.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-177.38333,-14.3306,174.1,71.3214]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["NOAA","Climate Normals","Weather","Surface Observations","Climatology","CONUS"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"NOAA National Centers for Environmental Information","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_climate_normals:period":["1981-2010","1991-2020","2006-2020"],"noaa_climate_normals:frequency":["hourly","daily","monthly","annualseasonal"]},"description":"The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981–2010)\n- Supplemental 15-year normals (2006–2020)\n","item_assets":{"geoparquet":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"noaanormals"}}},"msft:region":"eastus","stac_version":"1.0.0","table:tables":[{"name":"1981_2010-hourly","description":"Hourly Climate Normals for Period 1981-2010"},{"name":"1981_2010-daily","description":"Daily Climate Normals for Period 1981-2010"},{"name":"1981_2010-monthly","description":"Monthly Climate Normals for Period 1981-2010"},{"name":"1981_2010-annualseasonal","description":"Annual/Seasonal Climate Normals for Period 1981-2010"},{"name":"1991_2020-hourly","description":"Hourly Climate Normals for Period 1991-2020"},{"name":"1991_2020-daily","description":"Daily Climate Normals for Period 1991-2020"},{"name":"1991_2020-monthly","description":"Monthly Climate Normals for Period 1991-2020"},{"name":"1991_2020-annualseasonal","description":"Annual/Seasonal Climate Normals for Period 1991-2020"},{"name":"2006_2020-hourly","description":"Hourly Climate Normals for Period 2006-2020"},{"name":"2006_2020-daily","description":"Daily Climate Normals for Period 2006-2020"},{"name":"2006_2020-monthly","description":"Monthly Climate Normals for Period 2006-2020"},{"name":"2006_2020-annualseasonal","description":"Annual/Seasonal Climate Normals for Period 2006-2020"}],"msft:group_id":"noaa-climate-normals","msft:container":"climate-normals-geoparquet","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"sci:publications":[{"doi":"10.1175/BAMS-D-11-00197.1","citation":"Arguez, A., I. Durre, S. Applequist, R. Vose, M. Squires, X. Yin, R. Heim, and T. Owen, 2012: NOAA's 1981-2010 climate normals: An overview. Bull. Amer. Meteor. Soc., 93, 1687-1697. DOI: 10.1175/BAMS-D-11-00197.1."},{"doi":"10.1175/BAMS-D-11-00173.1","citation":"Applequist, S., A. Arguez, I. Durre, M. Squires, R. Vose, and X. Yin, 2012: 1981-2010 U.S. Hourly Normals. Bulletin of the American Meteorological Society, 93, 1637-1640. DOI: 10.1175/BAMS-D-11-00173.1."}],"msft:storage_account":"noaanormals","msft:short_description":"Tabular Climate Normal data for over 15,000 United States weather station locations in GeoParquet format."},{"id":"noaa-climate-normals-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals","type":"text/html","title":"NOAA U.S. Climate Normals Landing Page"},{"rel":"license","href":"https://www.noaa.gov/information-technology/open-data-dissemination","type":"text/html","title":"NOAA Open Data Dissemination"},{"rel":"about","href":"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf","type":"application/pdf","title":"Readme for Monthly Gridded Normals"},{"rel":"about","href":"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf","type":"application/pdf","title":"Documentation for Monthly Gridded Normals"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA US Gridded Climate Normals (NetCDF)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-netcdf-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA U.S. Gridded Climate Normals (NetCDF) Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-climate-normals-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-124.708333,24.541666,-66.999995,49.375001]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["NOAA","Climate Normals","Weather","Surface Observations","Climatology","CONUS"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"NOAA National Centers for Environmental Information","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"noaa_climate_normals:period":["1901-2000","1991-2020","2006-2020"],"noaa_climate_normals:frequency":["daily","monthly"]},"description":"The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901–2020, 1991–2020, and 2006–2020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991–2020, and 2006–2020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n    - 100-year (1901–2000)\n    - 30-year (1991–2020)\n    - 15-year (2006–2020)\n- Daily normals\n    - 30-year (1991–2020)\n    - 15-year (2006–2020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n","item_assets":{"netcdf":{"type":"application/netcdf","roles":["data"]}},"msft:region":"eastus","stac_version":"1.0.0","msft:group_id":"noaa-climate-normals","msft:container":"gridded-normals","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"noaanormals","msft:short_description":"Gridded Climate Normals for the contiguous United States in the original NetCDF format provided by NOAA's National Centers for Environmental Information."},{"id":"goes-glm","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm"},{"rel":"license","href":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527","title":"License"},{"rel":"about","href":"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527","type":"text/html","title":"Product Landing Page"},{"rel":"about","href":"https://www.goes-r.gov/users/docs/PUG-main-vol1.pdf","type":"application/pdf","title":"Product Definition and Users' Guide (PUG) Vol.1 Main"},{"rel":"about","href":"https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf","type":"application/pdf","title":"Product Definition and Users' Guide (PUG) Vol.5 Level 2+ Products"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5KH0KK6"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/goes-glm","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"GOES-R Lightning Detection","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-glm-thumb.png","type":"image/png","roles":["thumbnail"],"title":"GOES GLM Thumbnail"},"geoparquet-items":{"href":"abfs://items/goes-glm.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"QS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-66.56,-8.44,66.56],[156.44,-66.56,180.0,66.56]]},"temporal":{"interval":[["2018-02-13T16:10:00Z",null]]}},"license":"proprietary","sci:doi":"10.7289/V5KH0KK6","keywords":["GOES","NOAA","NASA","Satellite","Lightning","Weather"],"providers":[{"url":"https://www.nasa.gov/content/goes","name":"NASA","roles":["producer"]},{"url":"https://www.goes-r.gov/","name":"NOAA","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[8000],"mission":["GOES"],"platform":["GOES-16","GOES-17","GOES-18","GOES-19"],"instruments":["FM1","FM2"],"constellation":["GOES"],"processing:level":["L2"],"goes:orbital-slot":["West","East","Test"]},"description":"The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).","item_assets":{"netcdf":{"type":"application/netcdf","roles":["data"],"title":"Original NetCDF4 file"}},"msft:region":"westeurope","sci:citation":"GOES-R Algorithm Working Group and GOES-R Series Program, (2018): NOAA GOES-R Series Geostationary Lightning Mapper (GLM) Level 2 Lightning Detection: Events, Groups, and Flashes. NOAA National Centers for Environmental Information. doi:10.7289/V5KH0KK6.","stac_version":"1.0.0","msft:group_id":"goes","msft:container":"noaa-goes-geoparquet","stac_extensions":["https://stac-extensions.github.io/goes/v1.0.0/schema.json","https://stac-extensions.github.io/processing/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"goeseuwest","msft:short_description":"Continuous lightning detection over the Western Hemisphere from the Geostationary Lightning Mapper (GLM) instrument."},{"id":"sentinel-1-grd","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd"},{"rel":"license","href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sentinel 1 Level-1 Ground Range Detected (GRD)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-1-grd.png","type":"image/png","roles":["thumbnail"],"title":"Sentinel 1 GRD"},"geoparquet-items":{"href":"abfs://items/sentinel-1-grd.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-10-10T00:28:21Z",null]]}},"license":"proprietary","keywords":["ESA","Copernicus","Sentinel","C-Band","SAR","GRD"],"providers":[{"url":"https://earth.esa.int/web/guest/home","name":"ESA","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"platform":["SENTINEL-1A","SENTINEL-1B","SENTINEL-1C"],"constellation":["Sentinel-1"],"s1:resolution":["full","high","medium"],"s1:orbit_source":["DOWNLINK","POEORB","PREORB","RESORB"],"sar:looks_range":[5,6,3,2],"sat:orbit_state":["ascending","descending"],"sar:product_type":["GRD"],"sar:looks_azimuth":[1,6,2],"sar:polarizations":[["VV","VH"],["HH","HV"],["VV"],["VH"],["HH"],["HV"]],"sar:frequency_band":["C"],"s1:processing_level":["1"],"sar:instrument_mode":["IW","EW","SM"],"sar:center_frequency":[5.405],"sar:resolution_range":[20,23,50,93,9],"s1:product_timeliness":["NRT-10m","NRT-1h","NRT-3h","Fast-24h","Off-line","Reprocessing"],"sar:resolution_azimuth":[22,23,50,87,9],"sar:pixel_spacing_range":[10,25,40,3.5],"sar:observation_direction":["right"],"sar:pixel_spacing_azimuth":[10,25,40,3.5],"sar:looks_equivalent_number":[4.4,29.7,2.7,10.7,3.7],"sat:platform_international_designator":["2014-016A","2016-025A","0000-000A"]},"description":"The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.","item_assets":{"hh":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","description":"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied."},"hv":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"HV: horizontal transmit, vertical receive","description":"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied."},"vh":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VH: vertical transmit, horizontal receive","description":"Amplitude of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied."},"vv":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VV: vertical transmit, vertical receive","description":"Amplitude of signal transmitted with vertical polarization and received with vertical polarization with radiometric terrain correction applied."},"thumbnail":{"type":"image/png","roles":["thumbnail"],"title":"Preview Image","description":"An averaged, decimated preview image in PNG format. Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the  co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations."},"safe-manifest":{"type":"application/xml","roles":["metadata"],"title":"Manifest File","description":"General product metadata in XML format. Contains a high-level textual description of the product and references to all of product's components, the product metadata, including the product identification and the resource references, and references to the physical location of each component file contained in the product."},"schema-noise-hh":{"type":"application/xml","roles":["metadata"],"title":"Noise Schema","description":"Estimated thermal noise look-up tables"},"schema-noise-hv":{"type":"application/xml","roles":["metadata"],"title":"Noise Schema","description":"Estimated thermal noise look-up tables"},"schema-noise-vh":{"type":"application/xml","roles":["metadata"],"title":"Noise Schema","description":"Estimated thermal noise look-up tables"},"schema-noise-vv":{"type":"application/xml","roles":["metadata"],"title":"Noise Schema","description":"Estimated thermal noise look-up tables"},"schema-product-hh":{"type":"application/xml","roles":["metadata"],"title":"Product Schema","description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc."},"schema-product-hv":{"type":"application/xml","roles":["metadata"],"title":"Product Schema","description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc."},"schema-product-vh":{"type":"application/xml","roles":["metadata"],"title":"Product Schema","description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc."},"schema-product-vv":{"type":"application/xml","roles":["metadata"],"title":"Product Schema","description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc."},"schema-calibration-hh":{"type":"application/xml","roles":["metadata"],"title":"Calibration Schema","description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."},"schema-calibration-hv":{"type":"application/xml","roles":["metadata"],"title":"Calibration Schema","description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."},"schema-calibration-vh":{"type":"application/xml","roles":["metadata"],"title":"Calibration Schema","description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."},"schema-calibration-vv":{"type":"application/xml","roles":["metadata"],"title":"Calibration Schema","description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration."}},"stac_version":"1.0.0","msft:group_id":"sentinel-1","msft:container":"s1-grd","stac_extensions":["https://stac-extensions.github.io/sar/v1.0.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"sentinel1euwest","msft:short_description":"Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model.","msft:region":"westeurope"},{"id":"noaa-climate-normals-gridded","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals","type":"text/html","title":"NOAA U.S. Climate Normals Landing Page"},{"rel":"license","href":"https://www.noaa.gov/information-technology/open-data-dissemination","type":"text/html","title":"NOAA Open Data Dissemination"},{"rel":"about","href":"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf","type":"application/pdf","title":"Readme for Monthly Gridded Normals"},{"rel":"about","href":"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf","type":"application/pdf","title":"Documentation for Monthly Gridded Normals"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-gridded-thumb.png","type":"image/png","roles":["thumbnail"],"title":"NOAA U.S. Gridded Climate Normals (Cloud-Optimized GeoTIFF) Thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-climate-normals-gridded.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-124.708333,24.541666,-66.999995,49.375001]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["NOAA","Climate Normals","Weather","Surface Observations","Climatology","CONUS"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"NOAA National Centers for Environmental Information","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_climate_normals:period":["1901-2000","1991-2020","2006-2020"],"noaa_climate_normals:frequency":["daily","monthly","seasonal","annual"]},"description":"The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901–2020, 1991–2020, and 2006–2020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991–2020, and 2006–2020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n    - 100-year (1901–2000)\n    - 30-year (1991–2020)\n    - 15-year (2006–2020)\n- Daily normals\n    - 30-year (1991–2020)\n    - 15-year (2006–2020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n| Abbreviation |               Description                |\n| ------------ | ---------------------------------------- |\n| prcp         | Precipitation over the time period       |\n| tavg         | Mean temperature over the time period    |\n| tmax         | Maximum temperature over the time period |\n| tmin         | Minimum temperature over the time period |\n\nAnd an \"aggregation\":\n\n| Abbreviation |                                  Description                                   |\n| ------------ | ------------------------------------------------------------------------------ |\n| max          | Maximum of the variable over the time period                                   |\n| min          | Minimum of the variable over the time period                                   |\n| std          | Standard deviation of the value over the time period                           |\n| flag         | An count of the number of inputs (months, years, etc.) to calculate the normal |\n| norm         | The normal for the variable over the time period                               |\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n","item_assets":{"prcp_max":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"prcp_min":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"prcp_std":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tavg_max":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tavg_min":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tavg_std":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmax_max":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmax_min":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmax_std":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmin_max":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmin_min":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmin_std":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"prcp_flag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"months","nodata":0,"data_type":"float32","spatial_resolution":5000}]},"prcp_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tavg_flag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"months","nodata":0,"data_type":"float32","spatial_resolution":5000}]},"tavg_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmax_flag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"months","nodata":0,"data_type":"float32","spatial_resolution":5000}]},"tmax_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"tmin_flag":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"months","nodata":0,"data_type":"float32","spatial_resolution":5000}]},"tmin_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"m2dprcp_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"description":"Month-To-Day Precipitation normals","raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]},"y2dprcp_norm":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"description":"Year-To-Day Precipitation normals","raster:bands":[{"unit":"millimeter","nodata":"nan","data_type":"float32","spatial_resolution":5000}]}},"msft:region":"eastus","stac_version":"1.0.0","msft:group_id":"noaa-climate-normals","msft:container":"gridded-normals-cogs","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json"],"msft:storage_account":"noaanormals","msft:short_description":"Gridded Climate Normals for the contiguous United States in Cloud Optimized GeoTIFF (COG) format."},{"id":"aster-l1t","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t"},{"rel":"license","href":"https://www.usgs.gov/core-science-systems/hdds/data-policy","title":"Public Domain"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/aster-l1t","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/aster-l1t","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ASTER L1T","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png","type":"image/png","roles":["thumbnail"],"title":"ASTER L1T"},"geoparquet-items":{"href":"abfs://items/aster-l1t.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"AS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-04T12:00:00Z","2006-12-31T12:00:00Z"]]}},"license":"proprietary","keywords":["ASTER","USGS","NASA","Satellite","Global"],"providers":[{"url":"https://terra.nasa.gov/about/terra-instruments/aster","name":"NASA","roles":["producer","licensor"]},{"url":"https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/aster-overview/","name":"USGS","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"summaries":{"gsd":[15,30,90],"eo:bands":[{"gsd":15,"name":"VNIR_Band1","common_name":"green","description":"visible yellow/green","center_wavelength":0.56,"full_width_half_max":0.08},{"gsd":15,"name":"VNIR_Band2","common_name":"red","description":"visible red","center_wavelength":0.66,"full_width_half_max":0.06},{"gsd":15,"name":"VNIR_Band3N","common_name":"nir08","description":"near infrared","center_wavelength":0.82,"full_width_half_max":0.08},{"gsd":30,"name":"SWIR_Band4","common_name":"swir16","description":"short-wave infrared","center_wavelength":1.65,"full_width_half_max":0.1},{"gsd":30,"name":"SWIR_Band5","common_name":"swir22","description":"short-wave infrared","center_wavelength":2.165,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band6","common_name":"swir22","description":"short-wave infrared","center_wavelength":2.205,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band7","common_name":"swir22","description":"short-wave infrared","center_wavelength":2.26,"full_width_half_max":0.05},{"gsd":30,"name":"SWIR_Band8","description":"short-wave infrared","center_wavelength":2.339,"full_width_half_max":0.07},{"gsd":30,"name":"SWIR_Band9","description":"short-wave infrared","center_wavelength":2.395,"full_width_half_max":0.07},{"gsd":90,"name":"TIR_Band10","common_name":"lwir","description":"thermal infrared","center_wavelength":8.3,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band11","common_name":"lwir","description":"thermal infrared","center_wavelength":8.65,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band12","common_name":"lwir","description":"thermal infrared","center_wavelength":9.11,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band13","common_name":"lwir","description":"thermal infrared","center_wavelength":10.6,"full_width_half_max":0.7},{"gsd":90,"name":"TIR_Band14","common_name":"lwir","description":"thermal infrared","center_wavelength":11.3,"full_width_half_max":0.7}],"platform":["terra"],"instruments":["aster"]},"description":"The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution.  ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006.  L1T images have been terrain-corrected and rotated to a north-up UTM projection.  Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n","item_assets":{"TIR":{"roles":["data"],"title":"TIR Swath data","eo:bands":[{"gsd":90,"name":"TIR_Band10","common_name":"lwir","description":"thermal infrared","center_wavelength":8.3,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band11","common_name":"lwir","description":"thermal infrared","center_wavelength":8.65,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band12","common_name":"lwir","description":"thermal infrared","center_wavelength":9.11,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band13","common_name":"lwir","description":"thermal infrared","center_wavelength":10.6,"full_width_half_max":0.7},{"gsd":90,"name":"TIR_Band14","common_name":"lwir","description":"thermal infrared","center_wavelength":11.3,"full_width_half_max":0.7}],"raster:bands":[{"name":"TIR_Band10","nodata":0,"data_type":"uint16","spatial_resolution":90},{"name":"TIR_Band11","nodata":0,"data_type":"uint16","spatial_resolution":90},{"name":"TIR_Band12","nodata":0,"data_type":"uint16","spatial_resolution":90},{"name":"TIR_Band13","nodata":0,"data_type":"uint16","spatial_resolution":90},{"name":"TIR_Band14","nodata":0,"data_type":"uint16","spatial_resolution":90}]},"xml":{"type":"application/xml","roles":["metadata"],"title":"XML metadata"},"SWIR":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"SWIR Swath data","eo:bands":[{"gsd":30,"name":"SWIR_Band4","common_name":"swir","description":"short-wave infrared","center_wavelength":1.65,"full_width_half_max":0.1},{"gsd":30,"name":"SWIR_Band5","common_name":"swir","description":"short-wave infrared","center_wavelength":2.165,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band6","common_name":"swir","description":"short-wave infrared","center_wavelength":2.205,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band7","common_name":"swir","description":"short-wave infrared","center_wavelength":2.26,"full_width_half_max":0.05},{"gsd":30,"name":"SWIR_Band8","common_name":"swir","description":"short-wave infrared","center_wavelength":2.339,"full_width_half_max":0.07},{"gsd":30,"name":"SWIR_Band9","common_name":"swir","description":"short-wave infrared","center_wavelength":2.395,"full_width_half_max":0.07}],"raster:bands":[{"name":"SWIR_Band4","nodata":0,"data_type":"uint8","spatial_resolution":30},{"name":"SWIR_Band5","nodata":0,"data_type":"uint8","spatial_resolution":30},{"name":"SWIR_Band6","nodata":0,"data_type":"uint8","spatial_resolution":30},{"name":"SWIR_Band7","nodata":0,"data_type":"uint8","spatial_resolution":30},{"name":"SWIR_Band8","nodata":0,"data_type":"uint8","spatial_resolution":30},{"name":"SWIR_Band9","nodata":0,"data_type":"uint8","spatial_resolution":30}]},"VNIR":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VNIR Swath data","eo:bands":[{"gsd":15,"name":"VNIR_Band1","common_name":"yellow/green","description":"visible yellow/green","center_wavelength":0.56,"full_width_half_max":0.08},{"gsd":15,"name":"VNIR_Band2","common_name":"red","description":"visible red","center_wavelength":0.66,"full_width_half_max":0.06},{"gsd":15,"name":"VNIR_Band3N","common_name":"near infrared","description":"near infrared","center_wavelength":0.82,"full_width_half_max":0.08}],"raster:bands":[{"name":"VNIR_Band1","nodata":0,"data_type":"uint8","spatial_resolution":15},{"name":"VNIR_Band2","nodata":0,"data_type":"uint8","spatial_resolution":15},{"name":"VNIR_Band3N","nodata":0,"data_type":"uint8","spatial_resolution":15}]},"qa-txt":{"type":"text/plain","roles":["metadata"],"title":"QA browse file","description":"Geometric quality assessment report."},"qa-browse":{"type":"image/jpeg","roles":["thumbnail"],"title":"QA browse file","description":"Single-band black and white reduced resolution browse overlaid with red, green, and blue (RGB) markers for GCPs used during the geometric verification quality check."},"tir-browse":{"type":"image/jpeg","roles":["thumbnail"],"title":"Standalone reduced resolution TIR"},"vnir-browse":{"type":"image/jpeg","roles":["thumbnail"],"title":"VNIR browse file","description":"Standalone reduced resolution VNIR"}},"stac_version":"1.0.0","msft:container":"aster","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"astersa","msft:short_description":"The ASTER instrument, launched on-board NASA's Terra satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution.  This dataset contains ASTER data from 2000-2006.","msft:region":"westeurope"},{"id":"cil-gdpcir-cc-by-sa","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa"},{"rel":"license","href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","type":"text/html","title":"Creative Commons Attribution Share Alike 4.0 International"},{"rel":"cite-as","href":"https://zenodo.org/record/6403794","type":"text/html"},{"rel":"describedby","href":"https://github.com/ClimateImpactLab/downscaleCMIP6/","type":"text/html","title":"Project homepage"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png","type":"image/png","title":"Thumbnail"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"license":"CC-BY-SA-4.0","sci:doi":"10.5194/egusphere-2022-1513","keywords":["CMIP6","Climate Impact Lab","Rhodium Group","Precipitation","Temperature"],"providers":[{"url":"https://impactlab.org/","name":"Climate Impact Lab","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"summaries":{"cmip6:variable":["pr","tasmax","tasmin"],"cmip6:source_id":["CanESM5"],"cmip6:experiment_id":["historical","ssp126","ssp245","ssp370","ssp585"],"cmip6:institution_id":["BCC","CAS","CCCma","CMCC","CSIRO","CSIRO-ARCCSS","DKRZ","EC-Earth-Consortium","INM","MIROC","MOHC","MPI-M","NCC","NOAA-GFDL","NUIST"]},"description":"The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40]              |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40]              |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40]              |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40]              |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]              |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40]              |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40]              |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]              |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]              |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-SA-40]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999–2049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)","item_assets":{"pr":{"type":"application/vnd+zarr","roles":["data"],"title":"Precipitation","description":"Precipitation"},"tasmax":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Maximum Near-Surface Air Temperature","description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Minimum Near-Surface Air Temperature","description":"Daily Minimum Near-Surface Air Temperature"}},"stac_version":"1.0.0","msft:group_id":"cil-gdpcir","cube:variables":{"pr":{"type":"data","unit":"mm day-1","attrs":{"units":"mm day-1"},"dimensions":["time","lat","lon"]},"tasmax":{"type":"data","unit":"K","attrs":{"units":"K","comment":"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')","long_name":"Daily Maximum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: maximum (interval: 5 minutes)","cell_measures":"area: areacella","original_name":"TREFHTMX","standard_name":"air_temperature"},"dimensions":["time","lat","lon"],"description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"data","unit":"K","attrs":{"units":"K","comment":"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')","long_name":"Daily Minimum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: minimum 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Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n","item_assets":{"image":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"RGBIR COG tile","eo:bands":[{"name":"Red","common_name":"red"},{"name":"Green","common_name":"green"},{"name":"Blue","common_name":"blue"},{"name":"NIR","common_name":"nir","description":"near-infrared"}]},"metadata":{"type":"text/plain","roles":["metadata"],"title":"FGDC 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This dataset includes NAIP images from 2010 to the present."},{"id":"io-lulc-9-class","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class"},{"rel":"related","href":"https://livingatlas.arcgis.com/landcover/"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"CC BY 4.0"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"10m Annual Land Use Land Cover (9-class) V1","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-9-class.png","title":"10m Annual Land Use Land Cover (9-class)","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/io-lulc-9-class.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Global","Land Cover","Land Use","Sentinel"],"providers":[{"url":"https://www.esri.com/","name":"Esri","roles":["licensor"]},{"url":"https://www.impactobservatory.com/","name":"Impact Observatory","roles":["processor","producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"raster:bands":[{"nodata":0,"spatial_resolution":10}]},"description":"__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model.  The Esri 2020 Land Cover map was also produced by Impact Observatory.  The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Global land cover data","file:values":[{"values":[0],"summary":"No Data"},{"values":[1],"summary":"Water"},{"values":[2],"summary":"Trees"},{"values":[4],"summary":"Flooded vegetation"},{"values":[5],"summary":"Crops"},{"values":[7],"summary":"Built area"},{"values":[8],"summary":"Bare ground"},{"values":[9],"summary":"Snow/ice"},{"values":[10],"summary":"Clouds"},{"values":[11],"summary":"Rangeland"}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"io-land-cover","msft:container":"io-lulc","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/label/v1.0.0/schema.json","https://stac-extensions.github.io/file/v2.1.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Global land cover information with 9 classes for 2017-2022 at 10m resolution"},{"id":"io-biodiversity","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"CC BY 4.0"},{"rel":"about","href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf","type":"application/pdf","title":"Technical White Paper"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/io-biodiversity","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Biodiversity Intactness","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-biodiversity-thumb.png","title":"Biodiversity Intactness","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/io-biodiversity.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Global","Biodiversity"],"providers":[{"url":"https://www.impactobservatory.com/","name":"Impact Observatory","roles":["processor","producer","licensor"]},{"url":"https://www.vizzuality.com/","name":"Vizzuality","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"version":["v1"]},"description":"Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled “Global 100m Projections of Biodiversity Intactness for the years 2017-2020.”\n\nAll years are available under a Creative Commons BY-4.0 license.\n","item_assets":{"data":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Biodiversity Intactness","description":"Terrestrial biodiversity intactness at 100m resolution","raster:bands":[{"sampling":"area","data_type":"float32","spatial_resolution":100}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:container":"impact","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"pcdata01euw","msft:short_description":"Global terrestrial biodiversity intactness at 100m resolution for years 2017-2020"},{"id":"noaa-cdr-sea-surface-temperature-whoi","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi"},{"rel":"license","href":"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea%20Surface%20Temperature%20-%20WHOI/UseAgreement_01B-27a.pdf","type":"application/pdf","title":"NOAA CDR Sea Surface Temperature - WHOI Use Agreement"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/climate-data-records/sea-surface-temperature-whoi","type":"text/html","title":"Sea Surface Temperature - WHOI CDR"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5FB510W"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-sea-surface-temperature-whoi","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sea Surface Temperature - WHOI CDR","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-sea-surface-temperature-whoi-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Sea Surface Temperature - WHOI CDR thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-cdr-sea-surface-temperature-whoi.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z",null]]}},"license":"proprietary","sci:doi":"10.7289/V5FB510W","keywords":["Global","Climate","NOAA","Ocean","Temperature"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"National Centers for Environmental Information","roles":["producer","processor","licensor"],"description":"NCEI is the Nation's leading authority for environmental data, and manage one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world. NCEI contributes to the NESDIS mission by developing new products and services that span the science disciplines and enable better data discovery."},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"description":"The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25° resolution grid over the global ice-free oceans from January 1988—present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n","item_assets":{"fill_missing_qc":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"nodata":"nan","data_type":"float32"}]},"sea_surface_temperature":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"raster:bands":[{"unit":"degree Celsius","nodata":"nan","data_type":"float32"}]}},"msft:region":"eastus","sci:citation":"Clayson, Carol Anne; Brown, Jeremiah; and NOAA CDR Program (2016). NOAA Climate Data Record (CDR) of Sea Surface Temperature - WHOI, Version 2. 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The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations."},{"id":"noaa-cdr-ocean-heat-content","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content"},{"rel":"license","href":"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Ocean_Heat_Content/UseAgreement_01B-41.pdf","type":"application/pdf","title":"NOAA CDR Ocean Heat Content Use Agreement"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/climate-data-records/global-ocean-heat-content","type":"text/html","title":"Global Ocean Heat Content CDR"},{"rel":"cite-as","href":"https://doi.org/10.7289/v53f4mvp"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-ocean-heat-content","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Global Ocean Heat Content CDR","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-ocean-heat-content-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Global Ocean Heat Content CDR thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-cdr-ocean-heat-content.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-90.0,180.0,90.0]]},"temporal":{"interval":[["1972-03-01T00:00:00Z","2022-03-31T23:59:59Z"]]}},"license":"proprietary","sci:doi":"10.7289/v53f4mvp","keywords":["Global","Climate","NOAA","Temperature","Ocean"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"National Centers for Environmental Information","roles":["producer","processor","licensor"],"description":"NCEI is the Nation's leading authority for environmental data, and manage one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world. NCEI contributes to the NESDIS mission by developing new products and services that span the science disciplines and enable better data discovery."},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_cdr:interval":["monthly","seasonal","yearly","pentadal"],"noaa_cdr:max_depth":[100,700,2000]},"description":"The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n","item_assets":{"heat_content":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Ocean Heat Content anomalies from WOA09","raster:bands":[{"unit":"10^18 joules","nodata":"nan","data_type":"float32"}]},"mean_salinity":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Mean salinity anomalies from WOA09","raster:bands":[{"nodata":"nan","data_type":"float32"}]},"mean_temperature":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Mean temperature anomalies from WOA09","raster:bands":[{"unit":"degrees Celsius","nodata":"nan","data_type":"float32"}]},"mean_halosteric_sea_level":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Mean halosteric sea level anomalies from WOA09","raster:bands":[{"unit":"millimeters","nodata":"nan","data_type":"float32"}]},"mean_thermosteric_sea_level":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Mean thermosteric sea level anomalies from WOA09","raster:bands":[{"unit":"millimeters","nodata":"nan","data_type":"float32"}]},"mean_total_steric_sea_level":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Mean total steric sea level anomalies from WOA09","raster:bands":[{"unit":"millimeters","nodata":"nan","data_type":"float32"}]}},"msft:region":"eastus","sci:citation":"Levitus, Sydney; Antonov, John I.; Boyer, Tim P.; Baranova, Olga K.; García, Hernán E.; Locarnini, Ricardo A.; Mishonov, Alexey V.; Reagan, James R.; [Seidov, Dan; Yarosh, Evgeney; Zweng, Melissa M. (2017). NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 0164586). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v53f4mvp.","stac_version":"1.0.0","msft:group_id":"noaa-cdr","msft:container":"ocean-heat-content","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"noaacdr","msft:short_description":"The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales."},{"id":"cil-gdpcir-cc0","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0"},{"rel":"license","href":"https://spdx.org/licenses/CC0-1.0.html","type":"text/html","title":"Creative Commons Zero v1.0 Universal"},{"rel":"cite-as","href":"https://zenodo.org/record/6403794","type":"text/html"},{"rel":"describedby","href":"https://github.com/ClimateImpactLab/downscaleCMIP6/","type":"text/html","title":"Project homepage"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png","type":"image/png","title":"Thumbnail"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"license":"CC0-1.0","sci:doi":"10.5194/egusphere-2022-1513","keywords":["CMIP6","Climate Impact Lab","Rhodium Group","Precipitation","Temperature"],"providers":[{"url":"https://impactlab.org/","name":"Climate Impact Lab","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"summaries":{"cmip6:variable":["pr","tasmax","tasmin"],"cmip6:source_id":["FGOALS-g3","INM-CM4-8","INM-CM5-0"],"cmip6:experiment_id":["historical","ssp126","ssp245","ssp370","ssp585"],"cmip6:institution_id":["BCC","CAS","CCCma","CMCC","CSIRO","CSIRO-ARCCSS","DKRZ","EC-Earth-Consortium","INM","MIROC","MOHC","MPI-M","NCC","NOAA-GFDL","NUIST"]},"description":"The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40               |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40               |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40               |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999–2049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n  under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)","item_assets":{"pr":{"type":"application/vnd+zarr","roles":["data"],"title":"Precipitation","description":"Precipitation"},"tasmax":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Maximum Near-Surface Air Temperature","description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Minimum Near-Surface Air Temperature","description":"Daily Minimum Near-Surface Air Temperature"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"cil-gdpcir","cube:variables":{"pr":{"type":"data","unit":"mm day-1","attrs":{"units":"mm day-1"},"dimensions":["time","lat","lon"]},"tasmax":{"type":"data","unit":"K","attrs":{"units":"K","comment":"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')","long_name":"Daily Maximum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: maximum (interval: 5 minutes)","cell_measures":"area: areacella","original_name":"TREFHTMX","standard_name":"air_temperature"},"dimensions":["time","lat","lon"],"description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"data","unit":"K","attrs":{"units":"K","comment":"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')","long_name":"Daily Minimum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: minimum (interval: 5 minutes)","cell_measures":"area: areacella","original_name":"TREFHTMN","standard_name":"air_temperature"},"dimensions":["time","lat","lon"],"description":"Daily Minimum Near-Surface Air Temperature"}},"msft:container":"cil-gdpcir","cube:dimensions":{"lat":{"axis":"y","step":0.25,"type":"spatial","extent":[-89.875,89.875],"reference_system":"epsg:4326"},"lon":{"axis":"x","step":0.25,"type":"spatial","extent":[-179.875,179.875],"reference_system":"epsg:4326"},"time":{"step":"P1DT0H0M0S","type":"temporal","extent":["1950-01-01T12:00:00Z","2100-12-31T12:00:00Z"],"description":"time"}},"stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"msft:storage_account":"rhgeuwest","msft:short_description":"Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"},{"id":"cil-gdpcir-cc-by","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by"},{"rel":"license","href":"https://spdx.org/licenses/CC-BY-4.0.html","type":"text/html","title":"Creative Commons Attribution 4.0 International"},{"rel":"cite-as","href":"https://zenodo.org/record/6403794","type":"text/html"},{"rel":"describedby","href":"https://github.com/ClimateImpactLab/downscaleCMIP6/","type":"text/html","title":"Project homepage"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png","type":"image/png","title":"Thumbnail"},"geoparquet-items":{"href":"abfs://items/cil-gdpcir-cc-by.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","sci:doi":"10.5194/egusphere-2022-1513","keywords":["CMIP6","Climate Impact Lab","Rhodium Group","Precipitation","Temperature"],"providers":[{"url":"https://impactlab.org/","name":"Climate Impact Lab","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"summaries":{"cmip6:variable":["pr","tasmax","tasmin"],"cmip6:source_id":["BCC-CSM2-MR","ACCESS-ESM1-5","ACCESS-CM2","MIROC-ES2L","MIROC6","NorESM2-LM","NorESM2-MM","GFDL-CM4","GFDL-ESM4","NESM3","MPI-ESM1-2-HR","HadGEM3-GC31-LL","UKESM1-0-LL","MPI-ESM1-2-LR","EC-Earth3","EC-Earth3-AerChem","EC-Earth3-CC","EC-Earth3-Veg","EC-Earth3-Veg-LR","CMCC-CM2-SR5","CMCC-ESM2"],"cmip6:experiment_id":["historical","ssp126","ssp245","ssp370","ssp585"],"cmip6:institution_id":["BCC","CAS","CCCma","CMCC","CSIRO","CSIRO-ARCCSS","DKRZ","EC-Earth-Consortium","INM","MIROC","MOHC","MPI-M","NCC","NOAA-GFDL","NUIST"]},"description":"The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution |   Source model    |           Available experiments            |   License collection   |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS                  | FGOALS-g3 [^1]    | SSP2-4.5, SSP3-7.0, and SSP5-8.5           | Public domain datasets |\n| INM                  | INM-CM4-8         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM                  | INM-CM5-0         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC                  | BCC-CSM2-MR       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| CMCC                 | CMCC-CM2-SR5      | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CMCC                 | CMCC-ESM2         | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40               |\n| CSIRO-ARCCSS         | ACCESS-CM2        | SSP2-4.5 and SSP3-7.0                      | CC-BY-40               |\n| CSIRO                | ACCESS-ESM1-5     | SSP1-2.6, SSP2-4.5, and SSP3-7.0           | CC-BY-40               |\n| MIROC                | MIROC-ES2L        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MIROC                | MIROC6            | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MOHC                 | HadGEM3-GC31-LL   | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| MOHC                 | UKESM1-0-LL       | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M                | MPI-ESM1-2-LR     | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| MPI-M/DKRZ [^2]      | MPI-ESM1-2-HR     | SSP1-2.6 and SSP5-8.5                      | CC-BY-40               |\n| NCC                  | NorESM2-LM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NCC                  | NorESM2-MM        | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NOAA-GFDL            | GFDL-CM4          | SSP2-4.5 and SSP5-8.5                      | CC-BY-40               |\n| NOAA-GFDL            | GFDL-ESM4         | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40               |\n| NUIST                | NESM3             | SSP1-2.6, SSP2-4.5, and SSP5-8.5           | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3         | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-AerChem | ssp370                                     | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-CC      | ssp245 and ssp585                          | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg     | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| EC-Earth-Consortium  | EC-Earth3-Veg-LR  | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40               |\n| CCCma                | CanESM5           | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5     | CC-BY-40[^3]           |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999–2049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n  License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n  CMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n  ScenarioMIP Citation:\n\n  > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n  License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n  License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n  CMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n  ScenarioMIP Citation:\n\n  > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n  License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n  CMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n  ScenarioMIP Citation:\n\n  > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n  License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n  CMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n  ScenarioMIP Citation:\n\n  > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n  License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n  CMIP Citation:\n\n  > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n  ScenarioMIP Citation:\n\n  > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n  License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n  License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n  CMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n  ScenarioMIP Citation:\n\n  > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n  License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n  License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n  License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n  License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n  License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n  CMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n  ScenarioMIP Citation:\n\n  > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n  License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n  CMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n  ScenarioMIP Citation:\n\n  > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n  License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n  CMIP Citation:\n\n  > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n  ScenarioMIP Citation:\n\n  > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n  License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n  CMIP Citation:\n\n  > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n  ScenarioMIP Citation:\n\n  > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n  License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n  CMIP Citation:\n\n  > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n  ScenarioMIP Citation:\n\n  > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n  License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n  CMIP Citation:\n\n  > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n  ScenarioMIP Citation:\n\n  > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n  License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n  CMIP Citation:\n\n  > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n  ScenarioMIP Citation:\n\n  > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Früh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n  License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n  CMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n  ScenarioMIP Citation:\n\n  > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, Jörg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; Müller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n  License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n  CMIP Citation:\n\n  > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n  ScenarioMIP Citation:\n\n  > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n  License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n  CMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n  ScenarioMIP Citation:\n\n  > Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n  License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n  CMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n  ScenarioMIP Citation:\n\n  > Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkevåg, Alf; Schwinger, Jörg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n  License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n  CMIP Citation:\n\n  > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n  ScenarioMIP Citation:\n\n  > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* **CanESM5**\n\n  License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n  under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n  CMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n  ScenarioMIP Citation:\n\n  > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)","item_assets":{"pr":{"type":"application/vnd+zarr","roles":["data"],"title":"Precipitation","description":"Precipitation"},"tasmax":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Maximum Near-Surface Air Temperature","description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"application/vnd+zarr","roles":["data"],"title":"Daily Minimum Near-Surface Air Temperature","description":"Daily Minimum Near-Surface Air Temperature"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"cil-gdpcir","cube:variables":{"pr":{"type":"data","unit":"mm day-1","attrs":{"units":"mm day-1"},"dimensions":["time","lat","lon"]},"tasmax":{"type":"data","unit":"K","attrs":{"units":"K","comment":"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')","long_name":"Daily Maximum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: maximum (interval: 5 minutes)","cell_measures":"area: areacella","original_name":"TREFHTMX","standard_name":"air_temperature"},"dimensions":["time","lat","lon"],"description":"Daily Maximum Near-Surface Air Temperature"},"tasmin":{"type":"data","unit":"K","attrs":{"units":"K","comment":"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')","long_name":"Daily Minimum Near-Surface Air Temperature","coordinates":"height","cell_methods":"area: mean time: minimum (interval: 5 minutes)","cell_measures":"area: areacella","original_name":"TREFHTMN","standard_name":"air_temperature"},"dimensions":["time","lat","lon"],"description":"Daily Minimum Near-Surface Air Temperature"}},"msft:container":"cil-gdpcir","cube:dimensions":{"lat":{"axis":"y","step":0.25,"type":"spatial","extent":[-89.875,89.875],"reference_system":"epsg:4326"},"lon":{"axis":"x","step":0.25,"type":"spatial","extent":[-179.875,179.875],"reference_system":"epsg:4326"},"time":{"step":"P1DT0H0M0S","type":"temporal","extent":["1950-01-01T12:00:00Z","2100-12-31T12:00:00Z"],"description":"time"}},"stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"msft:storage_account":"rhgeuwest","msft:short_description":"Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"},{"id":"noaa-cdr-sea-surface-temperature-whoi-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi-netcdf"},{"rel":"license","href":"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea%20Surface%20Temperature%20-%20WHOI/UseAgreement_01B-27a.pdf","type":"application/pdf","title":"NOAA CDR Sea Surface Temperature - WHOI Use Agreement"},{"rel":"about","href":"https://www.ncei.noaa.gov/products/climate-data-records/sea-surface-temperature-whoi","type":"text/html","title":"Sea Surface Temperature - WHOI CDR"},{"rel":"cite-as","href":"https://doi.org/10.7289/V5FB510W"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-sea-surface-temperature-whoi-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sea Surface Temperature - WHOI CDR NetCDFs","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-sea-surface-temperature-whoi-netcdf-thumb.png","type":"image/png","roles":["thumbnail"],"title":"Sea Surface Temperature - WHOI (NetCDF) CDR thumbnail"},"geoparquet-items":{"href":"abfs://items/noaa-cdr-sea-surface-temperature-whoi-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180.0,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z",null]]}},"license":"proprietary","sci:doi":"10.7289/V5FB510W","keywords":["Global","Climate","NOAA","Ocean","Temperature"],"providers":[{"url":"https://www.ncei.noaa.gov/","name":"National Centers for Environmental Information","roles":["producer","processor","licensor"],"description":"NCEI is the Nation's leading authority for environmental data, and manage one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world. 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The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH<sub>4</sub>) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO<sub>2</sub>) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O<sub>3</sub>) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O<sub>3</sub>) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO<sub>2</sub>) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n","item_assets":{"co":{"type":"application/x-netcdf","roles":["data"],"title":"Carbon Monoxide Total Column"},"o3":{"type":"application/x-netcdf","roles":["data"],"title":"Ozone Total Column"},"ch4":{"type":"application/x-netcdf","roles":["data"],"title":"Methane Total 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Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n","item_assets":{"iwv":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"Oa18","description":"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. 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NCEI contributes to the NESDIS mission by developing new products and services that span the science disciplines and enable better data discovery."},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"summaries":{"noaa_cdr:interval":["monthly","seasonal","yearly","pentadal"],"noaa_cdr:max_depth":[100,700,2000]},"description":"The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n","item_assets":{"netcdf":{"type":"application/netcdf","roles":["data"]}},"msft:region":"eastus","sci:citation":"Levitus, Sydney; Antonov, John I.; Boyer, Tim P.; Baranova, Olga K.; García, Hernán E.; Locarnini, Ricardo A.; Mishonov, Alexey V.; Reagan, James R.; [Seidov, Dan; Yarosh, Evgeney; Zweng, Melissa M. (2017). NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 0164586). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v53f4mvp.","stac_version":"1.0.0","msft:group_id":"noaa-cdr","msft:container":"ocean-heat-content","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"noaacdr","msft:short_description":"The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales."},{"id":"met-office-uk-deterministic-pressure","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-pressure/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-pressure"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedby","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-pressure","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-pressure/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-pressure","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Pressure levels collection Met Office UKV 2km deterministic forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-uk-deterministic-pressure.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Pressure levels collection Met Office UKV 2km deterministic forecast Thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-uk-deterministic-pressure.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-13.7254,48.8643,4.3408,61.6102]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["Met Office","Weather","Forecast","UK","Temperature","Wind","Pressure","Humidity"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 7 parameters at 33 available pressure levels (from 100000Pa to 1000Pa) from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid.\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Pressure Levels\nAvailable pressure levels, in Pascals (Pa), are:\n* 100000.0 97500.0 95000.0 92500.0 90000.0 85000.0 80000.0 75000.0 70000.0 65000.0 60000.0 55000.0 50000.0 45000.0 40000.0 37500.0 35000.0 32500.0 30000.0 27500.0 25000.0 22500.0 20000.0 17500.0 15000.0 12500.0 10000.0 7000.0 5000.0 4000.0 3000.0 2000.0 1000.0\nExceptions:\n* Wet bulb potential temperature on pressure levels (wet_bulb_potential_temperature_on_pressure_levels) available pressure levels, in Pascals (Pa), are 85000.0 70000.0 50000.0\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643°N, 10.6734°W \n* Northwest: 61.3322°N, 13.7254°W \n* Northeast: 61.6102°N, 4.3408°E \n* Southeast: 49.0594°N, 2.4654°E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours \n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"height_ASL_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Height Above Sea Level on pressure levels","forecast:variable":"geopotential_height"},"wind_speed_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind speed on pressure levels","forecast:variable":"wind_speed"},"temperature_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Temperature on pressure levels","forecast:variable":"air_temperature"},"wind_direction_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind direction on pressure levels","forecast:variable":"wind_from_direction"},"relative_humidity_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Relative humidity on pressure levels","forecast:variable":"relative_humidity"},"geopotential_height_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Geopotential height on pressure levels","forecast:variable":"geopotential_height"},"wet_bulb_potential_temperature_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wet bulb potential temperature on pressure levels","forecast:variable":"wet_bulb_potential_temperature"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-uk-deterministic","cube:variables":{"wind_speed":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Wind speed on pressure levels is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. 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In meteorological reports the direction of the wind vector is given as the direction from which it is blowing."},"wet_bulb_potential_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Wet bulb potential temperature on pressure levels. 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The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n","item_assets":{"ntc-aod":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"SYN_440","description":"OLCI channel Oa03","center_wavelength":0.4425,"full_width_half_max":0.01},{"name":"SYN_550","description":"SLSTR nadir and oblique channel S1","center_wavelength":0.55,"full_width_half_max":0.02},{"name":"SYN_670","description":"SLSTR nadir and oblique channel S2","center_wavelength":0.659,"full_width_half_max":0.02},{"name":"SYN_865","description":"OLCI channel Oa17, SLSTR nadir and oblique channel 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S6","center_wavelength":1.665,"full_width_half_max":0.085}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["OLCI","SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["synergy-v10"],"s3:product_type":["SY_2_V10___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n","item_assets":{"ag":{"type":"application/x-netcdf","roles":["data"],"description":"Aerosol optical thickness 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transition","center_wavelength":0.70875,"full_width_half_max":0.01},{"name":"Oa12","description":"Band 12 - O2 absorption / clouds, vegetation","center_wavelength":0.75375,"full_width_half_max":0.0075},{"name":"Oa17","description":"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration","center_wavelength":0.865,"full_width_half_max":0.02},{"name":"Oa18","description":"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring","center_wavelength":0.885,"full_width_half_max":0.01},{"name":"Oa19","description":"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)","center_wavelength":0.9,"full_width_half_max":0.01}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["OLCI"],"constellation":["Sentinel-3"],"s3:product_name":["olci-lfr"],"s3:product_type":["OL_2_LFR___"],"sat:orbit_state":["descending","ascending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n","item_assets":{"iwv":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"Oa18","description":"Band 18 - Water vapour absorption reference. 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Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n","item_assets":{"safe-manifest":{"type":"application/xml","roles":["metadata"],"description":"SAFE product manifest"},"reduced-measurement":{"type":"application/x-netcdf","roles":["data"],"description":"Reduced measurement data file","s3:altimetry_bands":[{"band_width":0.32,"description":"Band Ku - 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SST, LST, Active fire","center_wavelength":10.854,"full_width_half_max":0.776},{"name":"S9","description":"Band 9 - SST, LST","center_wavelength":12.0225,"full_width_half_max":0.905}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["slstr-lst"],"s3:product_type":["SL_2_LST___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n","item_assets":{"lst-in":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"S8","description":"Band 8 - SST, LST, Active fire","center_wavelength":10.854,"full_width_half_max":0.776},{"name":"S9","description":"Band 9 - SST, LST","center_wavelength":12.0225,"full_width_half_max":0.905}],"description":"Land Surface Temperature (LST) values"},"slstr-met-tx":{"type":"application/x-netcdf","roles":["data"],"description":"Meteorological parameters regridded onto the 16km tie points"},"safe-manifest":{"type":"application/xml","roles":["metadata"],"description":"SAFE product manifest"},"slstr-time-in":{"type":"application/x-netcdf","roles":["data"],"description":"Time annotations for the 1km grid"},"slstr-flags-in":{"type":"application/x-netcdf","roles":["data"],"description":"Global flags for the 1km TIR grid, nadir view"},"lst-ancillary-ds":{"type":"application/x-netcdf","roles":["data"],"description":"LST ancillary measurement dataset"},"slstr-indices-in":{"type":"application/x-netcdf","roles":["data"],"description":"Scan, pixel and detector indices annotations for the 1km TIR grid, nadir view"},"slstr-geodetic-in":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution geodetic coordinates for the 1km TIR grid, nadir view"},"slstr-geodetic-tx":{"type":"application/x-netcdf","roles":["data"],"description":"16km geodetic coordinates"},"slstr-geometry-tn":{"type":"application/x-netcdf","roles":["data"],"description":"16km solar and satellite geometry annotations, nadir view"},"slstr-cartesian-in":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution cartesian coordinates for the 1km TIR grid, nadir view"},"slstr-cartesian-tx":{"type":"application/x-netcdf","roles":["data"],"description":"16km cartesian coordinates"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"sentinel-3","msft:container":"sentinel-3","stac_extensions":["https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/eo/v1.1.0/schema.json"],"msft:storage_account":"sentinel3euwest","msft:short_description":"Sentinel-3 land surface temperature (SLSTR LST)."},{"id":"sentinel-3-slstr-wst-l2-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf"},{"rel":"license","href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","type":"application/pdf","title":"Sentinel Data License"},{"rel":"about","href":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst","type":"text/html","title":"Sentinel-3 Sea Surface Temperature Product User Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-wst-l2-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sentinel-3 Sea Surface Temperature","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-wst-l2-netcdf-thumb.png","title":"Sentinel-3 Sea Surface Temperature Thumbnail","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/sentinel-3-slstr-wst-l2-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC Items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"MS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-10-31T23:59:57.451604Z",null]]}},"license":"proprietary","keywords":["Sentinel","Copernicus","ESA","Satellite","Temperature","Ocean"],"providers":[{"url":"https://earth.esa.int/web/guest/home","name":"ESA","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"eo:bands":[{"name":"S7","description":"Band 7 - SST, LST, Active fire","center_wavelength":3.742,"full_width_half_max":0.398},{"name":"S8","description":"Band 8 - SST, LST, Active fire","center_wavelength":10.854,"full_width_half_max":0.776},{"name":"S9","description":"Band 9 - SST, LST","center_wavelength":12.0225,"full_width_half_max":0.905}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["slstr-wst"],"s3:product_type":["SL_2_WST___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n","item_assets":{"l2p":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"S7","description":"Band 7 - SST, LST, Active fire","center_wavelength":3.742,"full_width_half_max":0.398},{"name":"S8","description":"Band 8 - SST, LST, Active fire","center_wavelength":10.854,"full_width_half_max":0.776},{"name":"S9","description":"Band 9 - SST, LST","center_wavelength":12.0225,"full_width_half_max":0.905}],"description":"Skin Sea Surface Temperature (SST) values"},"browse-jpg":{"type":"image/jpeg","roles":["thumbnail"],"description":"Preview image produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)"},"eop-metadata":{"type":"application/xml","roles":["metadata"],"description":"Metadata produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)"},"safe-manifest":{"type":"application/xml","roles":["metadata"],"description":"SAFE product manifest"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"sentinel-3","msft:container":"sentinel-3","stac_extensions":["https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/eo/v1.1.0/schema.json"],"msft:storage_account":"sentinel3euwest","msft:short_description":"Sentinel-3 sea surface temperature (SLSTR WST)."},{"id":"sentinel-3-sral-wat-l2-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf"},{"rel":"license","href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","type":"application/pdf","title":"Sentinel Data License"},{"rel":"about","href":"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types","type":"text/html","title":"Sentinel-3 Ocean Radar Altimetry Product User Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-wat-l2-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sentinel-3 Ocean Radar Altimetry","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-sral-wat-l2-netcdf-thumb.png","title":"Sentinel-3 Ocean Radar Altimetry Thumbnail","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/sentinel-3-sral-wat-l2-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC Items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"QS"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-81.5,180,81.5]]},"temporal":{"interval":[["2017-01-28T00:59:14.149496Z",null]]}},"license":"proprietary","keywords":["Sentinel","Copernicus","ESA","Satellite","Radar","Altimetry","Ocean"],"providers":[{"url":"https://earth.esa.int/web/guest/home","name":"ESA","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["SRAL"],"constellation":["Sentinel-3"],"s3:product_name":["sral-wat"],"s3:product_type":["SR_2_WAT___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n","item_assets":{"eop-metadata":{"type":"application/xml","roles":["metadata"],"description":"Product metadata file produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)"},"safe-manifest":{"type":"application/xml","roles":["metadata"],"description":"SAFE product manifest"},"reduced-measurement":{"type":"application/x-netcdf","roles":["data"],"description":"Reduced measurement data file","s3:altimetry_bands":[{"band_width":0.32,"description":"Band Ku - Range measurements","frequency_band":"Ku","center_frequency":13.575000064}]},"enhanced-measurement":{"type":"application/x-netcdf","roles":["data"],"description":"Enhanced measurement data file","s3:altimetry_bands":[{"band_width":0.29,"description":"Band C - Ionospheric correction","frequency_band":"C","center_frequency":5.409999872},{"band_width":0.32,"description":"Band Ku - Range measurements","frequency_band":"Ku","center_frequency":13.575000064}]},"standard-measurement":{"type":"application/x-netcdf","roles":["data"],"description":"Standard measurement data file","s3:altimetry_bands":[{"band_width":0.29,"description":"Band C - Ionospheric correction","frequency_band":"C","center_frequency":5.409999872},{"band_width":0.32,"description":"Band Ku - Range measurements","frequency_band":"Ku","center_frequency":13.575000064}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"sentinel-3","msft:container":"sentinel-3","stac_extensions":["https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"msft:storage_account":"sentinel3euwest","msft:short_description":"Sentinel-3 radar altimetry over ocean (SRAL WAT)."},{"id":"hls2-s30","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30"},{"rel":"license","href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","title":"LP DAAC - Data Citation and Policies"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/hls2-s30","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Harmonized Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hls2-s30.webp","type":"image/webp","title":"HLS2 Sentinel Collection Thumbnail"},"geoparquet-items":{"href":"abfs://items/hls2-s30.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"W-MON"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z",null]]}},"license":"proprietary","sci:doi":"10.5067/HLS/HLSS30.002","keywords":["Sentinel","Landsat","HLS","Satellite","Global","Imagery"],"providers":[{"url":"https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2","name":"ESA","roles":["producer"]},{"url":"https://doi.org/10.5067/HLS/HLSL30.002","name":"LP DAAC","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"gsd":[30],"platform":["Sentinel-2A","Sentinel-2B","Sentinel-2C"]},"description":"Harmonized Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance (SR)\nand top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the\njoint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI)\naboard the ESA (European Space Agency) Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\nThe combined measurement enables global observations of the land every 2-3 days at 30 meter\n(m) spatial resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution and\nMilitary Grid Reference System (MGRS) tiling system and are \"stackable\" for time series analysis.\nThis dataset is in the form of cloud-optimized GeoTIFFs. The HLS v2.0 data is generated by NASA's\nIMPACT team at Marshall Space Flight Center. The product latency is 1.7 days, from the satellite\noverpass to the HLS data availability at NASA's Land Processes Distributed Active Archive Center\n(LP DAAC). For more information see LP DAAC's\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\n\nThis collection contains HLS data collected from Sentinel-2A, Sentinel-2B, and Sentinel-2C.\nHLS data generated from Landsat can be found in a separate collection.\n","item_assets":{"B01":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Coastal Aerosol","eo:bands":[{"name":"B01","common_name":"coastal","center_wavelength":0.4439,"full_width_half_max":0.027}]},"B02":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Blue","eo:bands":[{"name":"B02","common_name":"blue","center_wavelength":0.4966,"full_width_half_max":0.098}]},"B03":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Green","eo:bands":[{"name":"B03","common_name":"green","center_wavelength":0.56,"full_width_half_max":0.045}]},"B04":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Red","eo:bands":[{"name":"B04","common_name":"red","center_wavelength":0.6645,"full_width_half_max":0.038}]},"B05":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Red-Edge 1","eo:bands":[{"name":"B05","center_wavelength":0.7039,"full_width_half_max":0.019}]},"B06":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Red-Edge 2","eo:bands":[{"name":"B06","center_wavelength":0.7402,"full_width_half_max":0.018}]},"B07":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Red-Edge 3","eo:bands":[{"name":"B07","center_wavelength":0.7825,"full_width_half_max":0.028}]},"B08":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"NIR Broad","eo:bands":[{"name":"B08","common_name":"nir","center_wavelength":0.8351,"full_width_half_max":0.145}]},"B09":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Water Vapor","eo:bands":[{"name":"B09","center_wavelength":0.945,"full_width_half_max":0.026}]},"B10":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Cirrus","eo:bands":[{"name":"B10","common_name":"cirrus","center_wavelength":1.3735,"full_width_half_max":0.075}]},"B11":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"SWIR 1","eo:bands":[{"name":"B11","common_name":"swir16","center_wavelength":1.6137,"full_width_half_max":0.143}]},"B12":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"SWIR 2","eo:bands":[{"name":"B12","common_name":"swir22","center_wavelength":2.22024,"full_width_half_max":0.242}]},"B8A":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"NIR Narrow","eo:bands":[{"name":"B8A","center_wavelength":0.8648,"full_width_half_max":0.033}]},"SAA":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"SAA","eo:bands":[{"name":"SAA"}]},"SZA":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"SZA","eo:bands":[{"name":"SZA"}]},"VAA":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VAA","eo:bands":[{"name":"VAA"}]},"VZA":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"VZA","eo:bands":[{"name":"VZA"}]},"Fmask":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Fmask","eo:bands":[{"name":"Fmask"}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"hls2","msft:container":"hls2","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/eo/v1.0.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/view/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"msft:storage_account":"hls2euwest","msft:short_description":"Harmonized Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data"},{"id":"ms-buildings","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings"},{"rel":"license","href":"https://github.com/microsoft/GlobalMLBuildingFootprints/blob/main/LICENSE","type":"text/html","title":"ODbL-1.0 License"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/ms-buildings","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Microsoft Building Footprints","assets":{"delta":{"href":"az://footprints/delta/2023-04-25/ml-buildings.parquet/","title":"Delta table","description":"Delta table with latest buildings footprints partitioned by Region and quadkey.","table:storage_options":{"account_name":"bingmlbuildings"}},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/msbuildings-thumbnail.png","type":"image/png","title":"Thumbnail"},"global-footprints":{"title":"Global Bing ML building footprints","href":"https://planetarycomputer.microsoft.com/api/data/v1/vector/collections/ms-buildings/tilesets/global-footprints/tilejson.json","type":"application/json","roles":["tiles"]}},"extent":{"spatial":{"bbox":[[-180.0,90.0,180.0,-90.0]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"ODbL-1.0","keywords":["Bing Maps","Buildings","geoparquet","Microsoft","Footprint","Delta"],"providers":[{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["producer","processor","host"]}],"summaries":{"msbuildings:processing-date":["2023-04-25"]},"description":"Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization – Converting building pixel detections into polygons\n\n**Stage 1: Semantic Segmentation**\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\n**Stage 2: Polygonization**\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Building Footprints","description":"Parquet dataset with the building footprints for this region.","table:storage_options":{"account_name":"bingmlbuildings"}}},"msft:region":"westeurope","stac_version":"1.0.0","table:columns":[{"name":"geometry","type":"byte_array","description":"Building footprint polygons"}],"msft:container":"footprints","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"bingmlbuildings","msft:short_description":"Machine learning detected buildings footprints."},{"id":"sentinel-3-slstr-frp-l2-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf"},{"rel":"license","href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","type":"application/pdf","title":"Sentinel Data License"},{"rel":"about","href":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp","type":"text/html","title":"Sentinel-3 Fire Radiative Power Product User Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-frp-l2-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sentinel-3 Fire Radiative Power","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-frp-l2-netcdf-thumb.png","title":"Sentinel-3 Fire Radiative Power Thumbnail","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/sentinel-3-slstr-frp-l2-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC Items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"W"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-08-08T23:11:15.617203Z",null]]}},"license":"proprietary","keywords":["Sentinel","Copernicus","ESA","Satellite","Temperature","Fire"],"providers":[{"url":"https://earth.esa.int/web/guest/home","name":"ESA","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"eo:bands":[{"name":"F1","description":"Band 10 - Active fire","center_wavelength":3.742,"full_width_half_max":0.398},{"name":"S5","description":"Band 5 - Cloud clearing, ice, snow, vegetation monitoring","center_wavelength":1.6134,"full_width_half_max":0.06068},{"name":"S6","description":"Band 6 - Vegetation state and cloud clearing","center_wavelength":2.2557,"full_width_half_max":0.05015},{"name":"S7","description":"Band 7 - SST, LST, Active fire","center_wavelength":3.742,"full_width_half_max":0.398}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["slstr-frp"],"s3:product_type":["SL_2_FRP___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n","item_assets":{"frp-in":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"S5","description":"Band 5 - Cloud clearing, ice, snow, vegetation monitoring","center_wavelength":1.6134,"full_width_half_max":0.06068},{"name":"S6","description":"Band 6 - Vegetation state and cloud clearing","center_wavelength":2.2557,"full_width_half_max":0.05015},{"name":"S7","description":"Band 7 - SST, LST, Active fire","center_wavelength":3.742,"full_width_half_max":0.398},{"name":"F1","description":"Band 10 - Active fire","center_wavelength":3.742,"full_width_half_max":0.398}],"description":"Fire Radiative Power (FRP) dataset"},"slstr-met-tx":{"type":"application/x-netcdf","roles":["data"],"description":"Meteorological parameters regridded onto the 16km tie points"},"safe-manifest":{"type":"application/xml","roles":["metadata"],"description":"SAFE product manifest"},"slstr-time-in":{"type":"application/x-netcdf","roles":["data"],"description":"Time annotations for the 1 KM grid"},"slstr-flags-fn":{"type":"application/x-netcdf","roles":["data"],"description":"Global flags for the 1km F1 grid, nadir view"},"slstr-flags-in":{"type":"application/x-netcdf","roles":["data"],"description":"Global flags for the 1km TIR grid, nadir view"},"slstr-indices-fn":{"type":"application/x-netcdf","roles":["data"],"description":"Scan, pixel and detector annotations for the 1km F1 grid, nadir view"},"slstr-indices-in":{"type":"application/x-netcdf","roles":["data"],"description":"Scan, pixel and detector annotations for the 1km TIR grid, nadir view"},"slstr-geodetic-fn":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution geodetic coordinates for the 1km F1 grid, nadir view"},"slstr-geodetic-in":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution geodetic coordinates for the 1km TIR grid, nadir view"},"slstr-geodetic-tx":{"type":"application/x-netcdf","roles":["data"],"description":"16km geodetic coordinates"},"slstr-geometry-tn":{"type":"application/x-netcdf","roles":["data"],"description":"16km solar and satellite geometry annotations, nadir view"},"slstr-cartesian-fn":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution cartesian coordinates for the 1km F1 grid, nadir view"},"slstr-cartesian-in":{"type":"application/x-netcdf","roles":["data"],"description":"Full resolution cartesian coordinates for the 1km TIR grid, nadir view"},"slstr-cartesian-tx":{"type":"application/x-netcdf","roles":["data"],"description":"16km cartesian coordinates"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"sentinel-3","msft:container":"sentinel-3","stac_extensions":["https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/eo/v1.1.0/schema.json"],"msft:storage_account":"sentinel3euwest","msft:short_description":"Sentinel-3 fire detection over land (SLSTR FRP)."},{"id":"sentinel-3-synergy-syn-l2-netcdf","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf"},{"rel":"license","href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","type":"application/pdf","title":"Sentinel Data License"},{"rel":"about","href":"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn","type":"text/html","title":"Sentinel-3 Land Surface Reflectance and Aerosol Product User Guide"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-syn-l2-netcdf","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Sentinel-3 Land Surface Reflectance and Aerosol","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-syn-l2-netcdf-thumb.png","title":"Sentinel-3 Land Surface Reflectance and Aerosol Thumbnail","media_type":"image/png"},"geoparquet-items":{"href":"abfs://items/sentinel-3-synergy-syn-l2-netcdf.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC Items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","msft:partition_info":{"is_partitioned":true,"partition_frequency":"W"},"table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-09-22T16:51:00.001276Z",null]]}},"license":"proprietary","keywords":["Sentinel","Copernicus","ESA","Satellite","Land","Reflectance","Aerosol"],"providers":[{"url":"https://earth.esa.int/web/guest/home","name":"ESA","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"eo:bands":[{"name":"Oa01","description":"Band 1 - Aerosol correction, improved water constituent retrieval","center_wavelength":0.4,"full_width_half_max":0.015},{"name":"Oa02","description":"Band 2 - Yellow substance and detrital pigments (turbidity)","center_wavelength":0.4125,"full_width_half_max":0.01},{"name":"Oa03","description":"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation","center_wavelength":0.4425,"full_width_half_max":0.01},{"name":"Oa04","description":"Band 4 - Chlorophyll","center_wavelength":0.49,"full_width_half_max":0.01},{"name":"Oa05","description":"Band 5 - Chlorophyll, sediment, turbidity, red tide","center_wavelength":0.51,"full_width_half_max":0.01},{"name":"Oa06","description":"Band 6 - Chlorophyll reference (minimum)","center_wavelength":0.56,"full_width_half_max":0.01},{"name":"Oa07","description":"Band 7 - Sediment loading","center_wavelength":0.62,"full_width_half_max":0.01},{"name":"Oa08","description":"Band 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation","center_wavelength":0.665,"full_width_half_max":0.01},{"name":"Oa09","description":"Band 9 - Improved fluorescence retrieval","center_wavelength":0.67375,"full_width_half_max":0.0075},{"name":"Oa10","description":"Band 10 - Chlorophyll fluorescence peak, red edge","center_wavelength":0.68125,"full_width_half_max":0.0075},{"name":"Oa11","description":"Band 11 - Chlorophyll fluorescence baseline, red edge transition","center_wavelength":0.70875,"full_width_half_max":0.01},{"name":"Oa12","description":"Band 12 - O2 absorption / clouds, vegetation","center_wavelength":0.75375,"full_width_half_max":0.0075},{"name":"Oa13","description":"Band 13 - O2 absorption / aerosol correction","center_wavelength":0.76125,"full_width_half_max":0.0025},{"name":"Oa16","description":"Band 16 - Atmospheric / aerosol correction","center_wavelength":0.77875,"full_width_half_max":0.015},{"name":"Oa17","description":"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration","center_wavelength":0.865,"full_width_half_max":0.02},{"name":"Oa18","description":"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring","center_wavelength":0.885,"full_width_half_max":0.01},{"name":"Oa19","description":"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)","center_wavelength":0.9,"full_width_half_max":0.01},{"name":"Oa21","description":"Band 21 - Water vapour absorption, atmospheric / aerosol correction","center_wavelength":1.02,"full_width_half_max":0.04},{"name":"S1","description":"Band 1 - Cloud screening, vegetation monitoring, aerosol","center_wavelength":0.55427,"full_width_half_max":0.01926},{"name":"S2","description":"Band 2 - NDVI, vegetation monitoring, aerosol","center_wavelength":0.65947,"full_width_half_max":0.01925},{"name":"S3","description":"Band 3 - NDVI, cloud flagging, pixel co-registration","center_wavelength":0.868,"full_width_half_max":0.0206},{"name":"S4","description":"Band 4 - Cirrus detection over land","center_wavelength":1.3748,"full_width_half_max":0.0208},{"name":"S5","description":"Band 5 - Cloud clearing, ice, snow, vegetation monitoring","center_wavelength":1.6134,"full_width_half_max":0.06068},{"name":"S6","description":"Band 6 - Vegetation state and cloud clearing","center_wavelength":2.2557,"full_width_half_max":0.05015},{"name":"SYN01","description":"OLCI channel Oa01","center_wavelength":0.4,"full_width_half_max":0.015},{"name":"SYN02","description":"OLCI channel Oa02","center_wavelength":0.4125,"full_width_half_max":0.01},{"name":"SYN03","description":"OLCI channel Oa03","center_wavelength":0.4425,"full_width_half_max":0.01},{"name":"SYN04","description":"OLCI channel Oa04","center_wavelength":0.49,"full_width_half_max":0.01},{"name":"SYN05","description":"OLCI channel Oa05","center_wavelength":0.51,"full_width_half_max":0.01},{"name":"SYN06","description":"OLCI channel Oa06","center_wavelength":0.56,"full_width_half_max":0.01},{"name":"SYN07","description":"OLCI channel Oa07","center_wavelength":0.62,"full_width_half_max":0.01},{"name":"SYN08","description":"OLCI channel Oa08","center_wavelength":0.665,"full_width_half_max":0.01},{"name":"SYN09","description":"OLCI channel Oa09","center_wavelength":0.67375,"full_width_half_max":0.0075},{"name":"SYN10","description":"OLCI channel Oa10","center_wavelength":0.68125,"full_width_half_max":0.0075},{"name":"SYN11","description":"OLCI channel Oa11","center_wavelength":0.70875,"full_width_half_max":0.01},{"name":"SYN12","description":"OLCI channel Oa12","center_wavelength":0.75375,"full_width_half_max":0.0075},{"name":"SYN13","description":"OLCI channel Oa16","center_wavelength":0.7785,"full_width_half_max":0.015},{"name":"SYN14","description":"OLCI channel Oa17","center_wavelength":0.865,"full_width_half_max":0.02},{"name":"SYN15","description":"OLCI channel Oa18","center_wavelength":0.885,"full_width_half_max":0.01},{"name":"SYN16","description":"OLCI channel Oa21","center_wavelength":1.02,"full_width_half_max":0.04},{"name":"SYN17","description":"SLSTR nadir channel S1","center_wavelength":0.555,"full_width_half_max":0.02},{"name":"SYN18","description":"SLSTR nadir channel S2","center_wavelength":0.659,"full_width_half_max":0.02},{"name":"SYN19","description":"SLSTR nadir channel S3","center_wavelength":0.865,"full_width_half_max":0.02},{"name":"SYN20","description":"SLSTR nadir channel S5","center_wavelength":1.61,"full_width_half_max":0.06},{"name":"SYN21","description":"SLSTR nadir channel S6","center_wavelength":2.25,"full_width_half_max":0.05},{"name":"SYN22","description":"SLSTR oblique channel S1","center_wavelength":0.555,"full_width_half_max":0.02},{"name":"SYN23","description":"SLSTR oblique channel S2","center_wavelength":0.659,"full_width_half_max":0.02},{"name":"SYN24","description":"SLSTR oblique channel S3","center_wavelength":0.865,"full_width_half_max":0.02},{"name":"SYN25","description":"SLSTR oblique channel S5","center_wavelength":1.61,"full_width_half_max":0.06},{"name":"SYN26","description":"SLSTR oblique channel S6","center_wavelength":2.25,"full_width_half_max":0.05}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["OLCI","SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["synergy-syn"],"s3:product_type":["SY_2_SYN___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n","item_assets":{"time":{"type":"application/x-netcdf","roles":["data"],"description":"Time stamps annotation"},"syn-amin":{"type":"application/x-netcdf","roles":["data"],"description":"L2 Aerosol model index number data"},"syn-flags":{"type":"application/x-netcdf","roles":["data"],"description":"Classification and quality Flags associated with OLCI, SLSTR and SYNERGY products"},"syn-ato550":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"S1","description":"Band 1 - Cloud screening, vegetation monitoring, aerosol","center_wavelength":0.55427,"full_width_half_max":0.01926},{"name":"S2","description":"Band 2 - NDVI, vegetation monitoring, aerosol","center_wavelength":0.65947,"full_width_half_max":0.01925},{"name":"S3","description":"Band 3 - NDVI, cloud flagging, pixel co-registration","center_wavelength":0.868,"full_width_half_max":0.0206},{"name":"S5","description":"Band 5 - Cloud clearing, ice, snow, vegetation monitoring","center_wavelength":1.6134,"full_width_half_max":0.06068},{"name":"S6","description":"Band 6 - Vegetation state and cloud clearing","center_wavelength":2.2557,"full_width_half_max":0.05015},{"name":"Oa01","description":"Band 1 - Aerosol correction, improved water constituent retrieval","center_wavelength":0.4,"full_width_half_max":0.015},{"name":"Oa02","description":"Band 2 - Yellow substance and detrital pigments (turbidity)","center_wavelength":0.4125,"full_width_half_max":0.01},{"name":"Oa03","description":"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation","center_wavelength":0.4425,"full_width_half_max":0.01},{"name":"Oa04","description":"Band 4 - Chlorophyll","center_wavelength":0.49,"full_width_half_max":0.01},{"name":"Oa05","description":"Band 5 - Chlorophyll, sediment, turbidity, red tide","center_wavelength":0.51,"full_width_half_max":0.01},{"name":"Oa06","description":"Band 6 - Chlorophyll reference (minimum)","center_wavelength":0.56,"full_width_half_max":0.01},{"name":"Oa07","description":"Band 7 - Sediment loading","center_wavelength":0.62,"full_width_half_max":0.01},{"name":"Oa08","description":"Band 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / 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Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n","item_assets":{"ag":{"type":"application/x-netcdf","roles":["data"],"description":"Aerosol optical thickness data"},"b0":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"B0","description":"OLCI channels Oa02, Oa03","center_wavelength":0.45,"full_width_half_max":0.02}],"description":"Top of atmosphere reflectance data set associated with the 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channels S5, S6","center_wavelength":1.665,"full_width_half_max":0.085}],"platform":["Sentinel-3A","Sentinel-3B"],"instruments":["OLCI","SLSTR"],"constellation":["Sentinel-3"],"s3:product_name":["synergy-vg1"],"s3:product_type":["SY_2_VG1___"],"sat:orbit_state":["ascending","descending"],"s3:processing_timeliness":["NT"],"sat:platform_international_designator":["2016-011A","2018-039A"]},"description":"This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n","item_assets":{"ag":{"type":"application/x-netcdf","roles":["data"],"description":"Aerosol optical thickness 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Report"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/esa-worldcover/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/esa-worldcover","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ESA WorldCover","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/esa-worldcover-thumb.png","type":"image/png","roles":["thumbnail"],"title":"ESA WorldCover thumbnail"},"geoparquet-items":{"href":"abfs://items/esa-worldcover.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet 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University"},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"mission":["sentinel-1","sentinel-2"],"platform":["sentinel-1a","sentinel-1b","sentinel-2a","sentinel-2b"],"instruments":["c-sar","msi"],"esa_worldcover:product_version":["1.0.0","2.0.0"]},"description":"The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n  - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n  - [WorldCover 2020 v100 Validation Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PVR_V1.1.pdf>)\n\n- WorldCover 2021 produced using v200 of the algorithm\n  - [WorldCover 2021 v200 User Manual](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf>)\n  - [WorldCover 2021 v200 Validaton Report](<https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PVR_V2.0.pdf>)\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n","item_assets":{"map":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Land Cover Classes","description":"Discrete classification according to the Land Cover Classification System scheme developed by the United Nations Food and Agriculture Organization","raster:bands":[{"name":"Band1","nodata":0,"sampling":"area","data_type":"uint8","description":"Classification values","spatial_resolution":10}],"classification:classes":[{"value":10,"color-hint":"006400","description":"Tree cover"},{"value":20,"color-hint":"FFBB22","description":"Shrubland"},{"value":30,"color-hint":"FFFF4C","description":"Grassland"},{"value":40,"color-hint":"F096FF","description":"Cropland"},{"value":50,"color-hint":"FA0000","description":"Built-up"},{"value":60,"color-hint":"B4B4B4","description":"Bare / sparse vegetation"},{"value":70,"color-hint":"F0F0F0","description":"Snow and ice"},{"value":80,"color-hint":"0064C8","description":"Permanent water bodies"},{"value":90,"color-hint":"0096A0","description":"Herbaceous wetland"},{"value":95,"color-hint":"00CF75","description":"Mangroves"},{"value":100,"color-hint":"FAE6A0","description":"Moss and lichen"}]},"input_quality":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["metadata"],"title":"Classification Input Data Quality","description":"Per pixel quality indicator showing the quality of the input data.","raster:bands":[{"name":"Band1","nodata":-1,"sampling":"area","data_type":"int16","description":"Number of Sentinel-1 GAMMA0 observations used in the classification workflow","spatial_resolution":60},{"name":"Band2","nodata":-1,"sampling":"area","data_type":"int16","description":"Number of Sentinel-2 L2A observations used in the classification workflow","spatial_resolution":60},{"name":"Band3","nodata":-1,"sampling":"area","data_type":"int16","description":"Percentage (0-100) of invalid S2 observations discarded in the classification workflow (after cloud and cloud shadow filtering)","spatial_resolution":60}]}},"msft:region":"westeurope","stac_version":"1.0.0","msft:container":"esa-worldcover","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/raster/v1.0.0/schema.json","https://stac-extensions.github.io/classification/v1.0.0/schema.json","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"msft:storage_account":"ai4edataeuwest","msft:short_description":"Global land cover product at 10 meter resolution based on Sentinel-1 and Sentinel-2 data"},{"id":"met-office-uk-deterministic-near-surface","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-near-surface/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-near-surface"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedby","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-near-surface","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-near-surface/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-near-surface","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Near-surface level collection Met Office UKV 2km deterministic forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-uk-deterministic-near-surface.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Near-surface level collection Met Office UKV 2km deterministic forecast Thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-uk-deterministic-near-surface.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-13.7254,48.8643,4.3408,61.6102]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["Met Office","Weather","Forecast","UK","Precipitation","Temperature","Wind","Pressure","Humidity"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 35 parameters at near-surface level from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid.\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. \n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area: \n* Southwest: 48.8643°N, 10.6734°W \n* Northwest: 61.3322°N, 13.7254°W \n* Northeast: 61.6102°N, 4.3408°E \n* Southeast: 49.0594°N, 2.4654°E \n\n## Data collection height\nThere are 3 forecast heights used within the near-surface this collection:\n* Surface: the default collection height\n* Screen level: 1.5m above the surface\n* Wind parameters: 10m above the surface\n\n## Timesteps\nFor most parameters, the following time steps are available, see exceptions below:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours\n\nExceptions (for `rate`, `accumulation`, `min` and `max` parameters):\n* Hourly hail fall accumulation (hail_fall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* Hail fall rate (hail_fall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Precipitation rate (precipitation_rate) has 15 minutely timesteps from 0 to 54 hours\n* Pressure at mean sea level (pressure_at_mean_sea_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Rainfall rate (rainfall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Hourly snowfall accumulation (snowfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall accumulation (snowfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours\n* Snowfall rate (snowfall_rate) has 15 minutely timesteps from 0 to 54 hours\n* Temperature at screen level (temperature_at_screen_level) has 15 minutely timesteps from 0 to 12 hours and hourly timesteps from 13 to 120 hours\n* Hourly temperature at screen level maximum (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 120 hours\n* Hourly temperature at screen level minimum (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 120 hours\n* Dew point temperature at screen level (temperature_of_dew_point_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Visibility at screen level (visibility_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Wind direction at 10m (wind_direction_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Wind gust at 10m (wind_gust_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly wind gust at 10m maximum (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H wind gust at 10m maximum(wind_gust_at_10m_max-PT03H) is only available every three hours from 57 to 120 hours\n* Wind speed at 10m (wind_speed_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. \n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: \n* variables store the data \n* dimensions give relevant dimension information for the variables \n* attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.  \n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. \n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.  \n\n## How to cite\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). \n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"landsea_mask":{"type":"application/netcdf","roles":["data"],"title":"Landsea mask","forecast:variable":"land_binary_mask"},"rainfall_rate":{"type":"application/netcdf","roles":["data"],"title":"Rainfall rate","forecast:variable":"rainfall_rate"},"snowfall_rate":{"type":"application/netcdf","roles":["data"],"title":"Snowfall rate","forecast:variable":"lwe_snowfall_rate"},"hail_fall_rate":{"type":"application/netcdf","roles":["data"],"title":"Hail fall rate","forecast:variable":"lwe_graupel_and_hail_fall_rate"},"wind_gust_at_10m":{"type":"application/netcdf","roles":["data"],"title":"Wind gust at 10m","forecast:variable":"wind_speed_of_gust"},"wind_speed_at_10m":{"type":"application/netcdf","roles":["data"],"title":"Wind speed at 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accumulation","forecast:variable":"lwe_thickness_of_graupel_and_hail_fall_amount"},"sensible_heat_flux_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Sensible heat flux at surface","forecast:variable":"surface_upward_sensible_heat_flux"},"precipitation_accumulation-PT01H":{"type":"application/netcdf","roles":["data"],"title":"Hourly precipitation accumulation","forecast:variable":"lwe_thickness_of_precipitation_amount"},"precipitation_accumulation-PT03H":{"type":"application/netcdf","roles":["data"],"title":"3H precipitation accumulation","forecast:variable":"lwe_thickness_of_precipitation_amount"},"relative_humidity_at_screen_level":{"type":"application/netcdf","roles":["data"],"title":"Relative humidity at screen level","forecast:variable":"relative_humidity"},"temperature_at_screen_level_max-PT01H":{"type":"application/netcdf","roles":["data"],"title":"Hourly temperature at screen level maximum","forecast:variable":"air_temperature"},"temperature_at_screen_level_min-PT01H":{"type":"application/netcdf","roles":["data"],"title":"Hourly temperature at screen level minimum","forecast:variable":"air_temperature"},"radiation_flux_in_uv_downward_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Radiation flux in uv downward at surface","forecast:variable":"surface_downwelling_ultraviolet_flux_in_air"},"temperature_of_dew_point_at_screen_level":{"type":"application/netcdf","roles":["data"],"title":"Dew point temperature at screen level","forecast:variable":"dew_point_temperature"},"radiation_flux_in_longwave_downward_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Radiation flux in longwave downward at surface","forecast:variable":"surface_downwelling_longwave_flux_in_air"},"radiation_flux_in_shortwave_total_downward_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Radiation flux in shortwave total downward at surface","forecast:variable":"surface_downwelling_shortwave_flux_in_air"},"radiation_flux_in_shortwave_direct_downward_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Radiation flux in shortwave direct downward at surface","forecast:variable":"surface_direct_downwelling_shortwave_flux_in_air"},"radiation_flux_in_shortwave_diffuse_downward_at_surface":{"type":"application/netcdf","roles":["data"],"title":"Radiation flux in shortwave diffuse downward at surface","forecast:variable":"surface_diffusive_downwelling_shortwave_flux_in_air"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-uk-deterministic","cube:variables":{"wind_speed":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Wind at 10m above the surface is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. The speed is the magnitude of velocity."},"rainfall_rate":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Instantaneous rate at which rain (as a depth) was being produced by the model precipitation scheme and deposited on the surface. This excludes the rain produced by the model convection scheme."},"air_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Minimum instantaneous air temperature at screen level (1.5m) in the previous hour."},"land_binary_mask":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Binary indicator of whether a  point is considered land (value = 1) or sea (value = 0). This is useful to identify the coastline and characterise points for correct interpretation of forecast data by users."},"surface_altitude":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Altitude or (geometric) height above the geoid of the surface (ground). It is worth remembering that orographic height can be negative, particularly in some of the inland sea areas (e.g. Caspian Sea at -27m and Dead Sea at -430m)."},"fog_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Here fog means a visibility of 1000 m or lower. The reduction in visibility is caused by  water droplets or minute ice crystals forming close to the surface. This quantity represents the fraction of horizontal  grid square occupied by fog. An alternative interpretation is that this represents the fractional probability of fog being present at any location in the grid square."},"lwe_snowfall_rate":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Instantaneous rate at which liquid water equivalent (LWE) snow (as a depth) was produced by the model precipitation scheme and deposited on the surface. This excludes the snow produced by the  model convection scheme."},"relative_humidity":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fractional relative humidity (ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water) at screen level (1.5m above the surface.)"},"visibility_in_air":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Horizontal distance at which something can be seen horizontally from screen level (1.5m)."},"wind_speed_of_gust":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Maximum diagnosed instantaneous wind gust at 10m in the previous 3 hours. This can be considered as the extreme wind speed that might be experienced in this period."},"surface_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Temperature at the surface interface between the air and the ground."},"wind_from_direction":{"type":"data","unit":"degrees","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Wind at 10m above the surface is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. In meteorological reports the direction of the wind vector is given as the direction from which it is blowing. Note: This with wind speed at 10m replaces x wind at 10m and y wind at 10m."},"surface_air_pressure":{"type":"data","unit":"Pa","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Air pressure at the surface (lower boundary of the atmosphere)."},"dew_point_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Instantaneous dew point temperature (temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity) at screen level (1.5m)."},"lwe_precipitation_rate":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Instantaneous rate at which liquid water (as a depth) was being deposited on the surface. This includes rain, snow and hail with the ice phase precipitation being considered as a liquid water equivalent (LWE)  value. It includes the contribution from the model convection scheme if this is invoked."},"air_pressure_at_sea_level":{"type":"data","unit":"Pa","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Air pressure at mean sea level which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL."},"thickness_of_rainfall_amount":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Implied depth of the rain produced by the model precipitation scheme which has been deposited on the surface in the previous 3 hours. This excludes the rain produced by the model convection scheme."},"lwe_graupel_and_hail_fall_rate":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Instantaneous rate at which liquid water equivalent (LWE) hail (as a depth) has been produced by the model precipitation scheme and deposited on the surface."},"lwe_thickness_of_snowfall_amount":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Implied depth of the (LWE) snow (as a depth) produced by the model precipitation scheme and deposited on the surface in the previous 3 hours. This excludes the snow produced by the model  convection scheme."},"surface_upward_sensible_heat_flux":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Exchange of heat between the surface and the air by motion of air, also called turbulent heat flux."},"lwe_thickness_of_surface_snow_amount":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Liquid water equivalent (LWE) depth of the snow lying on the surface (ground). Typically water is 10 times as dense as snow, so multiplying by 10 gives an approximate depth of the snow, although wet snow can be significantly denser and powder snow much less dense."},"lwe_thickness_of_precipitation_amount":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Implied depth of the layer of liquid water which has been deposited on the surface in the previous 3 hours. This includes rain, snow and hail, with the ice phase precipitation being considered as a liquid water equivalent (LWE)  value. It includes the contribution from the model convection scheme if this is invoked."},"surface_downwelling_longwave_flux_in_air":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Longwave radiation at the surface from above directed at the ground."},"surface_downwelling_shortwave_flux_in_air":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Total shortwave radiation at the surface from above directed at the ground."},"surface_downwelling_ultraviolet_flux_in_air":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Ultraviolet  radiation at the surface from above directed at the ground."},"lwe_thickness_of_graupel_and_hail_fall_amount":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Implied depth of the layer of liquid water equivalent (LWE) hail produced by the model precipitation scheme and deposited on the surface in the previous hour."},"surface_direct_downwelling_shortwave_flux_in_air":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Direct component of shortwave radiation at the surface from above directed at the ground."},"surface_diffusive_downwelling_shortwave_flux_in_air":{"type":"data","unit":"W/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Diffuse component of shortwave radiation at the surface from above directed at the ground."}},"msft:container":"deterministic","cube:dimensions":{"latitude":{"axis":"y","type":"spatial","extent":[50,62]},"longitude":{"axis":"x","type":"spatial","extent":[5,15]},"forecast:horizon":{"type":"temporal","extent":[null,null],"values":["PT0H","PT1H","PT2H","PT3H","PT4H","PT5H","PT6H","PT7H","PT8H","PT9H","PT10H","PT11H","PT12H","PT13H","PT14H","PT15H","PT16H","PT17H","PT18H","PT19H","PT20H","PT21H","PT22H","PT23H","PT24H","PT25H","PT26H","PT27H","PT28H","PT29H","PT30H","PT31H","PT32H","PT33H","PT34H","PT35H","PT36H","PT37H","PT38H","PT39H","PT40H","PT41H","PT42H","PT43H","PT44H","PT45H","PT46H","PT47H","PT48H","PT49H","PT50H","PT51H","PT52H","PT53H","PT54H","PT57H","PT60H","PT63H","PT66H","PT69H","PT72H","PT75H","PT78H","PT81H","PT84H","PT87H","PT90H","PT93H","PT96H","PT99H","PT102H","PT105H","PT108H","PT111H","PT114H","PT117H","PT120H"]},"forecast:reference_datetime":{"type":"temporal","extent":["2023-12-15T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/forecast/v0.2.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"ukmoeuwest","msft:short_description":"Met Office UK Deterministic Near Surface Level"},{"id":"met-office-global-deterministic-near-surface","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-near-surface/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-near-surface"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedby","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-near-surface","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-near-surface/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-near-surface","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Near-surface level collection Met Office global deterministic 10km forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-global-deterministic-near-surface.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Near-surface level collection Met Office global deterministic 10km forecast thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-global-deterministic-near-surface.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["Met Office","Global","Forecast","Cloud","Fog","Heat Flux","Precipitation","Pressure","Radiation","Rainfall","Humidity","Snow","Temperature","Wind"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 48 parameters at near-surface level from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points).\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Data collection height\nThere are 3 forecast heights used within the near-surface this collection:\n* Surface: the default collection height\n* Screen level: 1.5m above the surface\n* Wind parameters: 10m above the surface\n\n## Timesteps\nFor most parameters, the following time steps are available, see exceptions below:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\nExceptions (for `accumulation`, `min`, `max` and `mean` parameters):\n* Height of orography (height_of_orography) is only available at 0H\n* Hourly latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT01H) is only available every hour from 1 to 54 hours\n* 3H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT03H) is only available every three hours from 57 to 144 hours\n* 6H latent heat flux at surface mean (latent_heat_flux_at_surface_mean-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 144 hours\n* 6H precipitation accumulation (precipitation_accumulation-PT06H) is only available every six hours from 150 to 168 hours\n* Radiation flux in longwave downward at surface (radiation_flux_in_longwave_downward_at_surface) has six hourly timesteps from 150 to 162 hours\n* Radiation flux in uv downward at surface (radiation_flux_in_uv_downward_at_surface) has three hourly timesteps from 0 to 144 hours and six hourly timesteps from 150 to 162 hours\n* Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 144 hours\n* 6H rainfall accumulation (rainfall_accumulation-PT06H) is only available every six hours from 150 to 168 hours \n* Hourly rainfall rate from convection max (rainfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H rainfall rate from convection max (rainfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H rainfall rate from convection max (rainfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly snowfall rate from convection max (snowfall_rate_from_convection_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall rate from convection max (snowfall_rate_from_convection_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H snowfall rate from convection max (snowfall_rate_from_convection_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT01H) is only available every hour from 1 to 54 hours\n* 3H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT03H) is only available every three hours from 57 to 144 hours\n* 6H snowfall rate from convection mean (snowfall_rate_from_convection_mean-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly temperature at screen level max (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 54 hours\n* 3H temperature at screen level max (temperature_at_screen_level_max-PT03H) is only available every three hours from 57 to 144 hours\n* 6H temperature at screen level max (temperature_at_screen_level_max-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly temperature at screen level min (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 54 hours\n* 3H remperature at screen level min (temperature_at_screen_level_min-PT03H) is only available every three hours from 57 to 144 hours\n* 6H temperature at screen level min (temperature_at_screen_level_min-PT06H) is only available every six hours from 150 to 168 hours\n* Hourly wind gust at 10m max (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours \n* 3H wind gust at 10m max (wind_gust_at_10m_max-PT03H) is only available every three hours from 3 to 144 hours\n* 6H wind gust at 10m max (wind_gust_at_10m_max-PT06H) is only available every six hours from 54 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"rainfall_rate":{"type":"application/netcdf","roles":["data"],"title":"Rainfall rate","forecast:variable":"rainfall_rate"},"snowfall_rate":{"type":"application/netcdf","roles":["data"],"title":"Snowfall rate","forecast:variable":"lwe_snowfall_rate"},"wind_gust_at_10m":{"type":"application/netcdf","roles":["data"],"title":"Wind gust at 10m","forecast:variable":"wind_speed_of_gust"},"wind_speed_at_10m":{"type":"application/netcdf","roles":["data"],"title":"Wind speed at 10m","forecast:variable":"wind_speed"},"precipitation_rate":{"type":"application/netcdf","roles":["data"],"title":"Precipitation rate","forecast:variable":"lwe_precipitation_rate"},"height_of_orography":{"type":"application/netcdf","roles":["data"],"title":"Height of 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This can be considered as the extreme wind speed that might be experienced in this period."},"surface_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Temperature at the surface interface between the air and the ground."},"wind_from_direction":{"type":"data","unit":"degrees","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Wind at 10m above the surface is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. In meteorological reports the direction of the wind vector is given as the direction from which it is blowing. 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For more information see LP DAAC's\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\n\nThis collection contains HLS data collected from Landsat-8 and Landsat-9.\nHLS data generated from Sentinel-2 satellites can be found in a separate collection.\n","item_assets":{"B01":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Coastal Aerosol","eo:bands":[{"name":"B01","common_name":"coastal","center_wavelength":0.48,"full_width_half_max":0.02}]},"B02":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Blue","eo:bands":[{"name":"B02","common_name":"blue","center_wavelength":0.44,"full_width_half_max":0.06}]},"B03":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Green","eo:bands":[{"name":"B03","common_name":"green","center_wavelength":0.56,"full_width_half_max":0.06}]},"B04":{"type":"image/tiff; 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This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). \n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Height Levels\nAvailable height levels, in metres (m) above ground, are: \n* 5.0 10.0 20.0 30.0 50.0 75.0 100.0 150.0 200.0 250.0 300.0 400.0 500.0 600.0 700.0 800.0 1000.0 1250.0 1500.0 1750.0 2000.0 2250.0 2500.0 2750.0 3000.0 3250.0 3500.0 3750.0 4000.0 4500.0 5000.0 5500.0 6000.0\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"cloud_amount_on_height_levels":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount on height levels","forecast:variable":"cloud_volume_fraction_in_atmosphere_layer"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-global-deterministic","cube:variables":{"cloud_volume_fraction_in_atmosphere_layer":{"type":"data","unit":"1","dimensions":["latitude","longitude","height","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud in layers centred on height levels. The levels are height above ground."}},"msft:container":"deterministic","cube:dimensions":{"height":{"type":"spatial","values":[5,10,20,30,50,75,100,150,200,250,300,400,500,600,700,800,1000,1250,1500,1750,2000,2250,2500,2750,3000,3250,3500,3750,4000,4500,5000,5500,6000]},"latitude":{"axis":"y","type":"spatial","extent":[-90,90]},"longitude":{"axis":"x","type":"spatial","extent":[-180,180]},"forecast:horizon":{"type":"temporal","extent":[null,null],"values":["PT0H","PT1H","PT2H","PT3H","PT4H","PT5H","PT6H","PT7H","PT8H","PT9H","PT10H","PT11H","PT12H","PT13H","PT14H","PT15H","PT16H","PT17H","PT18H","PT19H","PT20H","PT21H","PT22H","PT23H","PT24H","PT25H","PT26H","PT27H","PT28H","PT29H","PT30H","PT31H","PT32H","PT33H","PT34H","PT35H","PT36H","PT37H","PT38H","PT39H","PT40H","PT41H","PT42H","PT43H","PT44H","PT45H","PT46H","PT47H","PT48H","PT49H","PT50H","PT51H","PT52H","PT53H","PT54H","PT57H","PT60H","PT63H","PT66H","PT69H","PT72H","PT75H","PT78H","PT81H","PT84H","PT87H","PT90H","PT93H","PT96H","PT99H","PT102H","PT105H","PT108H","PT111H","PT114H","PT117H","PT120H","PT123H","PT126H","PT129H","PT132H","PT135H","PT138H","PT141H","PT144H","PT150H","PT156H","PT162H","PT168H"]},"forecast:reference_datetime":{"type":"temporal","extent":["2023-12-15T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/forecast/v0.2.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"ukmoeuwest","msft:short_description":"Met Office Global Deterministic Height Level"},{"id":"met-office-global-deterministic-pressure","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-pressure/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-pressure"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedBy","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-pressure","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-pressure/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-pressure","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Pressure levels collection Met Office Global 10km deterministic weather forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-global-deterministic-pressure.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Pressure levels collection Met Office Global 10km deterministic weather forecast thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-global-deterministic-pressure.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["MetOffice","Global","Cloud"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 8 parameters at 33 available pressure levels (from 100000Pa to 1000Pa) from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points).\n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Pressure Levels\nAvailable pressure levels, in Pascals (Pa), are:\n* 100000.0 97500.0 95000.0 92500.0 90000.0 85000.0 80000.0 75000.0 70000.0 65000.0 60000.0 55000.0 50000.0 45000.0 40000.0 37500.0 35000.0 32500.0 30000.0 27500.0 25000.0 22500.0 20000.0 17500.0 15000.0 12500.0 10000.0 7000.0 5000.0 4000.0 3000.0 2000.0 1000.0 500.0 200.0 100.0 40.0\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions)\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"height_ASL_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Height Above Sea Level on pressure levels","forecast:variable":"geopotential_height"},"wind_speed_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind speed on pressure levels","forecast:variable":"wind_speed"},"temperature_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Temperature on pressure levels","forecast:variable":"air_temperature"},"wind_direction_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind direction on pressure levels","forecast:variable":"wind_from_direction"},"relative_humidity_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Relative humidity on pressure levels","forecast:variable":"relative_humidity"},"geopotential_height_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Geopotential height on pressure levels","forecast:variable":"geopotential_height"},"wind_vertical_velocity_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind vertical velocity on pressure levels","forecast:variable":"upward_air_velocity"},"wet_bulb_potential_temperature_on_pressure_levels":{"type":"application/netcdf","roles":["data"],"title":"Wet bulb potential temperature on pressure levels","forecast:variable":"wet_bulb_potential_temperature"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-global-deterministic","cube:variables":{"wind_speed":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Wind speed on pressure levels is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. The speed is the magnitude of velocity."},"air_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Air temperature on pressure levels"},"relative_humidity":{"type":"data","unit":"1","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Fractional relative humidity (ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water) on pressure levels"},"geopotential_height":{"type":"data","unit":"m","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Height above sea level of the pressure levels. Note: from January 2026, this parameter will be replaced by geopotential_height_on_pressure_levels"},"upward_air_velocity":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Speed of the vertical component of the air motion at a pressure levels. Upwards is positive and downwards is negative."},"wind_from_direction":{"type":"data","unit":"degrees","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Wind direction on pressure levels is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. In meteorological reports the direction of the wind vector is given as the direction from which it is blowing."},"wet_bulb_potential_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","pressure_levels","forecast:reference_datetime","forecast:horizon"],"description":"Wet bulb potential temperature on pressure levels. Wet bulb potential temperature is defined as the temperature that a parcel of air would have if it were brought down to the standard pressure of 1000hPa following a saturated adiabatic lapse rate."}},"msft:container":"deterministic","cube:dimensions":{"latitude":{"axis":"y","type":"spatial","extent":[-90,90]},"longitude":{"axis":"x","type":"spatial","extent":[-180,180]},"pressure_levels":{"type":"spatial","values":[100000,97500,95000,92500,90000,85000,80000,75000,70000,6500,60000,55000,50000,45000,40000,37500,35000,32500,30000,27500,25000,22500,20000,17500,15000,12500,10000,7000,5000,4000,3000,2000,1000]},"forecast:horizon":{"type":"temporal","extent":[null,null],"values":["PT0H","PT1H","PT2H","PT3H","PT4H","PT5H","PT6H","PT7H","PT8H","PT9H","PT10H","PT11H","PT12H","PT13H","PT14H","PT15H","PT16H","PT17H","PT18H","PT19H","PT20H","PT21H","PT22H","PT23H","PT24H","PT25H","PT26H","PT27H","PT28H","PT29H","PT30H","PT31H","PT32H","PT33H","PT34H","PT35H","PT36H","PT37H","PT38H","PT39H","PT40H","PT41H","PT42H","PT43H","PT44H","PT45H","PT46H","PT47H","PT48H","PT49H","PT50H","PT51H","PT52H","PT53H","PT54H","PT57H","PT60H","PT63H","PT66H","PT69H","PT72H","PT75H","PT78H","PT81H","PT84H","PT87H","PT90H","PT93H","PT96H","PT99H","PT102H","PT105H","PT108H","PT111H","PT114H","PT117H","PT120H","PT123H","PT126H","PT129H","PT132H","PT135H","PT138H","PT141H","PT144H","PT150H","PT156H","PT162H","PT168H"]},"forecast:reference_datetime":{"type":"temporal","extent":["2023-12-15T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/forecast/v0.2.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"ukmoeuwest","msft:short_description":"Met Office Global Deterministic Pressure Level"},{"id":"met-office-uk-deterministic-height","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-height/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-height"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedby","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-height","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-height/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-height","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Height levels collection Met Office UKV 2km deterministic forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-uk-deterministic-height.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Height levels collection Met Office UKV 2km deterministic forecast thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-uk-deterministic-height.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-13.7254,48.8643,4.3408,61.6102]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["Met Office","Weather","Forecast","UK","Cloud","Temperature","Wind","Height"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 4 parameters at 33 available height levels (5m to 6000m) from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files.\n\n## Height Levels\nAvailable height levels, in metres (m) above ground, are: \n* 5.0 10.0 20.0 30.0 40.0 50.0 60.0 75.0 100.0 125.0 150.0 175.0 200.0 225.0 250.0 275.0 300.0 350.0 400.0 450.0 500.0 600.0 700.0 800.0 900.0 1000.0 1125.0 1250.0 1375.0 1500.0 1625.0 1750.0 1875.0 2000.0 2125.0 2250.0 2375.0 2500.0 2625.0 2750.0 2875.0 3000.0 3125.0 3250.0 3375.0 3500.0 3625.0 3750.0 3875.0 4000.0 4500.0 5000.0 5500.0 6000.0 6750.0 7500.0\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643°N, 10.6734°W \n* Northwest: 61.3322°N, 13.7254°W \n* Northeast: 61.6102°N, 4.3408°E \n* Southeast: 49.0594°N, 2.4654°E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours \n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"wind_speed_on_height_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind speed on height levels","forecast:variable":"wind_speed"},"temperature_on_height_levels":{"type":"application/netcdf","roles":["data"],"title":"Temperature on height levels","forecast:variable":"air_temperature"},"cloud_amount_on_height_levels":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount on height levels","forecast:variable":"cloud_volume_fraction_in_atmosphere_layer"},"wind_direction_on_height_levels":{"type":"application/netcdf","roles":["data"],"title":"Wind direction on height levels","forecast:variable":"wind_from_direction"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-uk-deterministic","cube:variables":{"wind_speed":{"type":"data","unit":"m/s","dimensions":["latitude","longitude","height","forecast:reference_datetime","forecast:horizon"],"description":"Wind speed on height levels is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. The speed is the magnitude of velocity."},"air_temperature":{"type":"data","unit":"K","dimensions":["latitude","longitude","height","forecast:reference_datetime","forecast:horizon"],"description":"Air temperature on height levels."},"wind_from_direction":{"type":"data","unit":"degrees","dimensions":["latitude","longitude","height","forecast:reference_datetime","forecast:horizon"],"description":"Wind direction on height levels is defined as a two-dimensional (horizontal) air velocity vector with no vertical component. In meteorological reports the direction of the wind vector is given as the direction from which it is blowing."},"cloud_volume_fraction_in_atmosphere_layer":{"type":"data","unit":"1","dimensions":["latitude","longitude","height","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud in layers centred on height levels."}},"msft:container":"deterministic","cube:dimensions":{"height":{"type":"spatial","values":[5,10,20,30,50,75,100,150,200,250,300,400,500,600,700,800,1000,1250,1500,1750,2000,2250,2500,2750,3000,3250,3500,3750,4000,4500,5000,5500,6000]},"latitude":{"axis":"y","type":"spatial","extent":[50,62]},"longitude":{"axis":"x","type":"spatial","extent":[5,15]},"forecast:horizon":{"type":"temporal","extent":[null,null],"values":["PT0H","PT1H","PT2H","PT3H","PT4H","PT5H","PT6H","PT7H","PT8H","PT9H","PT10H","PT11H","PT12H","PT13H","PT14H","PT15H","PT16H","PT17H","PT18H","PT19H","PT20H","PT21H","PT22H","PT23H","PT24H","PT25H","PT26H","PT27H","PT28H","PT29H","PT30H","PT31H","PT32H","PT33H","PT34H","PT35H","PT36H","PT37H","PT38H","PT39H","PT40H","PT41H","PT42H","PT43H","PT44H","PT45H","PT46H","PT47H","PT48H","PT49H","PT50H","PT51H","PT52H","PT53H","PT54H","PT57H","PT60H","PT63H","PT66H","PT69H","PT72H","PT75H","PT78H","PT81H","PT84H","PT87H","PT90H","PT93H","PT96H","PT99H","PT102H","PT105H","PT108H","PT111H","PT114H","PT117H","PT120H"]},"forecast:reference_datetime":{"type":"temporal","extent":["2023-12-15T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/forecast/v0.2.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"ukmoeuwest","msft:short_description":"Met Office UK Deterministic Height Level"},{"id":"met-office-uk-deterministic-whole-atmosphere","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-whole-atmosphere/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-whole-atmosphere"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedby","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-whole-atmosphere","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-uk-deterministic-whole-atmosphere/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-whole-atmosphere","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Whole Atmosphere collection Met Office UKV 2km deterministic forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-uk-deterministic-whole-atmosphere.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Whole Atmosphere collection Met Office UKV 2km deterministic forecast thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-uk-deterministic-whole-atmosphere.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-13.7254,48.8643,4.3408,61.6102]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["Met Office","Weather","Forecast","UK","CAPE","Cloud","Freezing","Wet Bulb","Lightning"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 11 whole-atmosphere parameters from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files.\n\n## Coverage area\nThe forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area:\n* Southwest: 48.8643°N, 10.6734°W \n* Northwest: 61.3322°N, 13.7254°W \n* Northeast: 61.6102°N, 4.3408°E \n* Southeast: 49.0594°N, 2.4654°E \n\n## Timesteps\nThe following time steps are available:\n* every hour from 0 to 54 hours\n* every 3 hours from 57 to 120 hours\n\nExceptions:\n* CAPE surface (CAPE_surface) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours\n* Hourly lightning flash accumulation (lightning_flash_accumulation-PT01H) is only available every hour from 1 to 54 hours\n \n## Update frequency\nThere are three lengths of model run, each with its own update frequency:\nThere are three lengths of model run, each with its own update frequency: \n* Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. \n* Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. \n* Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC.\n\n## Archive length and latency\n\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 3-6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* variables store the data\n* dimensions give relevant dimension information for the variables\n* attributes provide auxiliary information about the variables or dataset itself\n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/) \n\n## Help us improve the data services we offer\n\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\n\nUKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\n\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"CAPE_surface":{"type":"application/netcdf","roles":["data"],"title":"CAPE surface","forecast:variable":"atmosphere_convective_available_potential_energy_wrt_surface"},"cloud_amount_of_low_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of low cloud","forecast:variable":"low_type_cloud_area_fraction"},"cloud_amount_of_high_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of high cloud","forecast:variable":"high_type_cloud_area_fraction"},"cloud_amount_of_total_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of total cloud","forecast:variable":"cloud_area_fraction"},"cloud_amount_of_medium_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of medium cloud","forecast:variable":"medium_type_cloud_area_fraction"},"height_AGL_at_freezing_level":{"type":"application/netcdf","roles":["data"],"title":"Height AGL at freezing level","forecast:variable":"freezing_level_height"},"cloud_amount_below_1000ft_ASL":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount below 1000ft ASL","forecast:variable":"cloud_area_fraction_assuming_only_consider_surface_to_1000_feet_asl"},"CAPE_most_unstable_below_500hPa":{"type":"application/netcdf","roles":["data"],"title":"CAPE most unstable below 500hPa","forecast:variable":"atmosphere_convective_available_potential_energy"},"lightning_flash_accumulation-PT01H":{"type":"application/netcdf","roles":["data"],"title":"Hourly lightning flash accumulation","forecast:variable":"number_of_lightning_flashes_per_unit_area"},"height_AGL_at_wet_bulb_freezing_level":{"type":"application/netcdf","roles":["data"],"title":"Height AGL at wet bulb freezing level","forecast:variable":"wet_bulb_freezing_level_height"},"height_AGL_at_cloud_base_where_cloud_cover_2p5_oktas":{"type":"application/netcdf","roles":["data"],"title":"Height AGL at cloud base where cloud cover 2p5 oktas","forecast:variable":"cloud_base_height_2p5_oktas"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-uk-deterministic","cube:variables":{"cloud_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud as diagnosed by the model cloud scheme. This is for the whole atmosphere column as seen from the surface or the top of the atmosphere."},"freezing_level_height":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Height of the 0 degrees celsius isotherm (freezing level) above the surface (ground)."},"cloud_base_height_2p5_oktas":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Height of the base of the lowest cloud above the surface (ground) where there is at least 2.5 oktas (eighths) of cloud cover. This corresponds to scattered cloud."},"low_type_cloud_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud in the low-level cloud height range (from the lowest model layer containing the 111m height level up to but excluding the lowest model layer containing 1949m height level)."},"high_type_cloud_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud in the high-level cloud height range (from the lowest model layer containing the 5574m height level up to but excluding the lowest model layer containing 13608m height level)."},"wet_bulb_freezing_level_height":{"type":"data","unit":"m","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Height of the wet bulb freezing level (i.e. where the wet bulb temperature is 0oC) above sea level. This is also referred to as the altitude of the wet bulb freezing level or (geometric) height above the geoid, which is the reference geopotential surface. Wet bulb temperature is defined as the temperature of a parcel of air cooled to saturation (100% relative humidity) by the evaporation of water into it with the latent heat supplied by the parcel."},"medium_type_cloud_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud in the mid-level cloud height range (from the lowest model layer containing the 1949m height level up to but excluding the lowest model layer containing 5574m height level)."},"number_of_lightning_flashes_per_unit_area":{"type":"data","unit":"count/m2","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"This represents the total (cloud-to-cloud and cloud-to-ground) lightning flashes per square metre within the previous hour"},"atmosphere_convective_available_potential_energy":{"type":"data","unit":"J/kg","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"CAPE (Convective Available Potential Energy) calculated for the most unstable parcel where the most unstable parcel is defined as the parcel with the highest fixed level CAPE launched from any level (including screen-level=1.5m) within 500hPa of the surface pressure."},"atmosphere_convective_available_potential_energy_wrt_surface":{"type":"data","unit":"J/kg","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Value of CAPE (Convective Available Potential Energy) calculated for a surface based parcel where a surface based parcel is defined as a parcel initiated with thermodynamic properties at screen level height (1.5m) i.e. the parcel is launched from screen level."},"cloud_area_fraction_assuming_only_consider_surface_to_1000_feet_asl":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud below 1000 feet above sea level."}},"msft:container":"deterministic","cube:dimensions":{"latitude":{"axis":"y","type":"spatial","extent":[50,62]},"longitude":{"axis":"x","type":"spatial","extent":[5,15]},"forecast:horizon":{"type":"temporal","extent":[null,null],"values":["PT0H","PT1H","PT2H","PT3H","PT4H","PT5H","PT6H","PT7H","PT8H","PT9H","PT10H","PT11H","PT12H","PT13H","PT14H","PT15H","PT16H","PT17H","PT18H","PT19H","PT20H","PT21H","PT22H","PT23H","PT24H","PT25H","PT26H","PT27H","PT28H","PT29H","PT30H","PT31H","PT32H","PT33H","PT34H","PT35H","PT36H","PT37H","PT38H","PT39H","PT40H","PT41H","PT42H","PT43H","PT44H","PT45H","PT46H","PT47H","PT48H","PT49H","PT50H","PT51H","PT52H","PT53H","PT54H","PT57H","PT60H","PT63H","PT66H","PT69H","PT72H","PT75H","PT78H","PT81H","PT84H","PT87H","PT90H","PT93H","PT96H","PT99H","PT102H","PT105H","PT108H","PT111H","PT114H","PT117H","PT120H"]},"forecast:reference_datetime":{"type":"temporal","extent":["2023-12-15T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/forecast/v0.2.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"ukmoeuwest","msft:short_description":"Met Office UK Deterministic Whole Atmosphere Level"},{"id":"era5-pds","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds"},{"rel":"license","href":"https://apps.ecmwf.int/datasets/licences/copernicus/","type":"application/pdf","title":"License to Use Copernicus Products"},{"rel":"describedby","href":"https://confluence.ecmwf.int/display/CKB/ERA5","type":"text/html","title":"Project homepage"},{"rel":"describedby","href":"https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it","type":"text/html","title":"How to cite"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/era5-pds","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/era5-pds","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"ERA5 - PDS","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/era5-thumbnail.png","type":"image/png","title":"Thumbnail"},"era5-static-var":{"href":"https://cpdataeuwest.blob.core.windows.net/era5/era5_static.nc","type":"application/netcdf","roles":["data"],"title":"ERA5 static variables","description":"ERA5 static variables (geopotential z, land-sea mask lsm, soil type slt) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS), licensed under CC BY (https://spdx.org/licenses/CC-BY-4.0) (DOI: 10.24381/cds.adbb2d47; accessed 2026-02-03). Attribution per CDS guidance."},"geoparquet-items":{"href":"abfs://items/era5-pds.parquet","roles":["stac-items"],"title":"GeoParquet STAC items","media_type":"application/x-parquet","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","extra_fields":{"msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["ERA5","ECMWF","Precipitation","Temperature","Reanalysis","Weather"],"providers":[{"url":"https://www.ecmwf.int/","name":"ECMWF","roles":["producer","licensor"]},{"url":"https://planetos.com/","name":"Planet OS","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"summaries":{"era5:kind":["fc","an"]},"description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An **analysis**, of the atmospheric conditions, is a blend of observations\n  with a previous forecast. An analysis can only provide\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n  parameters (parameters valid at a specific time, e.g temperature at 12:00),\n  but not accumulated parameters, mean rates or min/max parameters.\n* A **forecast** starts with an analysis at a specific time (the 'initialization\n  time'), and a model computes the atmospheric conditions for a number of\n  'forecast steps', at increasing 'validity times', into the future. A forecast\n  can provide\n  [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n  parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).","item_assets":{"surface_air_pressure":{"type":"application/vnd+zarr","roles":["data"],"title":"Surface pressure","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"cpdataeuwest"}}},"sea_surface_temperature":{"type":"application/vnd+zarr","roles":["data"],"title":"Sea surface temperature","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"cpdataeuwest"}}},"eastward_wind_at_10_metres":{"type":"application/vnd+zarr","roles":["data"],"title":"10 metre U wind 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downwards"}},"msft:container":"era5","cube:dimensions":{"lat":{"axis":"y","step":-0.25,"type":"spatial","extent":[-90,90],"description":"latitude","reference_system":"epsg:4326"},"lon":{"axis":"x","step":0.25,"type":"spatial","extent":[0,359.75],"description":"longitude","reference_system":"epsg:4326"},"time":{"step":"P0DT1H0M0S","type":"temporal","extent":["1970-01-01T00:00:00Z",null]}},"stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"msft:storage_account":"cpdataeuwest","msft:short_description":"A comprehensive reanalysis, which assimilates as many observations as possible in the upper air and near surface."},{"id":"met-office-global-deterministic-whole-atmosphere","type":"Collection","links":[{"rel":"items","type":"application/geo+json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-whole-atmosphere/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-whole-atmosphere"},{"rel":"license","href":"https://creativecommons.org/licenses/by-sa/4.0/deed.en","type":"text/html","title":"Creative Commons Attribution-ShareAlike 4.0"},{"rel":"describedBy","href":"https://www.metoffice.gov.uk/services/data/external-data-channels","title":"Met Office Dataset Documentation"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-whole-atmosphere","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/met-office-global-deterministic-whole-atmosphere/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/met-office-global-deterministic-whole-atmosphere","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Whole Atmosphere collection Met Office Global 10km deterministic weather forecast","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/met-office-global-deterministic-whole-atmosphere.jpg","type":"image/jpeg","roles":["thumbnail"],"title":"Whole Atmosphere collection Met Office Global 10km deterministic weather forecast thumbnail"},"geoparquet-items":{"href":"abfs://items/met-office-global-deterministic-whole-atmosphere.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection's STAC items exported to GeoParquet format.","table:storage_options":{"account_name":"pcstacitems"}}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-12-15T00:00:00Z",null]]}},"license":"proprietary","keywords":["MetOffice","Global","Cloud"],"providers":[{"url":"https://www.metoffice.gov.uk/","name":"Met Office","roles":["producer","licensor","processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"This collection offers 14 whole-atmosphere parameters from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). \n\nThe data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.\n\n## Timesteps\nThe following timesteps are available:\n* every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions)\n* every 3 hours from 57 to 144 hours\n* every 6 hours from 150 to 168 hours\n\n## Update Frequency\nThe model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC).\n\nThe runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours.\n\nThe forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available.\n\n## Archive length and latency\nAs of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date.\n\nThe data is typically available 6 hours after the model run time.\n\n## Technical specs\nThe data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components:\n* Variables store the data \n* Dimensions give relevant dimension information for the variables\n* Attributes provide auxiliary information about the variables or dataset itself \n\nNetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers.\n\nIris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data.\n\n[Find further support on using Iris with NetCDF files.](https://scitools-iris.readthedocs.io/en/stable/)\n\n## Help us improve the data services we offer\n[Join the Met Office research panel](https://forms.office.com/Pages/ResponsePage.aspx?id=YYHxF9cgRkeH_VD-PjtmGdxioYGoFbFIkZuB_q8Fb3VUQkoxRVQzTFdUMzNMVzczWVM5VTc3QTY3MC4u) to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.\n\n## How to cite\nMet Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897).\n\n## License\nBritish Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en).\n\n## Providers\n[Met Office](https://www.metoffice.gov.uk/)\n\nSee all datasets managed by [Met Office.](https://planetarycomputer.microsoft.com/catalog?filter=met+office)\n\n## Contact\n[servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). \n\nAs a non-operational service we aim to respond to any service support enquiries within 3-5 business days.\n","item_assets":{"CIN_surface":{"type":"application/netcdf","roles":["data"],"title":"CIN surface","forecast:variable":"atmosphere_convective_inhibition_wrt_surface"},"CAPE_surface":{"type":"application/netcdf","roles":["data"],"title":"CAPE surface","forecast:variable":"atmosphere_convective_available_potential_energy_wrt_surface"},"pressure_at_tropopause":{"type":"application/netcdf","roles":["data"],"title":"Pressure at tropopause","forecast:variable":"tropopause_air_pressure"},"cloud_amount_of_low_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of low cloud","forecast:variable":"low_type_cloud_area_fraction"},"temperature_at_tropopause":{"type":"application/netcdf","roles":["data"],"title":"Temperature at tropopause","forecast:variable":"tropopause_air_temperature"},"cloud_amount_of_high_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of high cloud","forecast:variable":"high_type_cloud_area_fraction"},"CIN_mixed_layer_lowest_500m":{"type":"application/netcdf","roles":["data"],"title":"CIN mixed layer lowest 500m","forecast:variable":"atmosphere_convective_inhibition"},"cloud_amount_of_total_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of total cloud","forecast:variable":"cloud_area_fraction"},"CAPE_mixed_layer_lowest_500m":{"type":"application/netcdf","roles":["data"],"title":"CAPE mixed layer lowest 500m","forecast:variable":"atmosphere_convective_available_potential_energy"},"cloud_amount_of_medium_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of medium cloud","forecast:variable":"medium_type_cloud_area_fraction"},"cloud_amount_below_1000ft_ASL":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount below 1000ft ASL","forecast:variable":"cloud_area_fraction_assuming_only_consider_surface_to_1000_feet_asl"},"CIN_most_unstable_below_500hPa":{"type":"application/netcdf","roles":["data"],"title":"CIN most unstable below 500hPa","forecast:variable":"atmosphere_convective_inhibition"},"CAPE_most_unstable_below_500hPa":{"type":"application/netcdf","roles":["data"],"title":"CAPE most unstable below 500hPa","forecast:variable":"atmosphere_convective_available_potential_energy"},"cloud_amount_of_total_convective_cloud":{"type":"application/netcdf","roles":["data"],"title":"Cloud amount of total convective cloud","forecast:variable":"convective_cloud_area_fraction"}},"msft:region":"westeurope","stac_version":"1.0.0","msft:group_id":"met-office-global-deterministic","cube:variables":{"cloud_area_fraction":{"type":"data","unit":"1","dimensions":["latitude","longitude","forecast:reference_datetime","forecast:horizon"],"description":"Fraction of horizontal grid square occupied by cloud as diagnosed by the model cloud scheme. 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