From 0ef3144b8bec5e6efa8eb72edb3880959622bcc1 Mon Sep 17 00:00:00 2001 From: Pete Gadomski Date: Mon, 28 Oct 2024 14:27:02 -0600 Subject: [PATCH] feat: add rudimentary crawling It only took about a minute on my computer on crappy wifi so this might be good enough for now? --- Cargo.lock | 1045 ++++++++++++++++++++++++++++++++++++++++++++-- Cargo.toml | 4 + crawl/.gitignore | 0 crawl/crawl.json | 1 + src/config.rs | 56 +++ src/crawl.rs | 118 ++++++ src/lib.rs | 56 +-- src/main.rs | 30 +- 8 files changed, 1216 insertions(+), 94 deletions(-) create mode 100644 crawl/.gitignore create mode 100644 crawl/crawl.json create mode 100644 src/config.rs create mode 100644 src/crawl.rs diff --git a/Cargo.lock b/Cargo.lock index c9b14a4..ffb7b10 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -2,6 +2,21 @@ # It is not intended for manual editing. version = 3 +[[package]] +name = "addr2line" +version = "0.24.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dfbe277e56a376000877090da837660b4427aad530e3028d44e0bffe4f89a1c1" +dependencies = [ + "gimli", +] + +[[package]] +name = "adler2" +version = "2.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "512761e0bb2578dd7380c6baaa0f4ce03e84f95e960231d1dec8bf4d7d6e2627" + [[package]] name = "android-tzdata" version = "0.1.1" @@ -53,7 +68,7 @@ version = "1.1.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "79947af37f4177cfead1110013d678905c37501914fba0efea834c3fe9a8d60c" dependencies = [ - "windows-sys", + "windows-sys 0.59.0", ] [[package]] @@ -63,7 +78,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2109dbce0e72be3ec00bed26e6a7479ca384ad226efdd66db8fa2e3a38c83125" dependencies = [ "anstyle", - "windows-sys", + "windows-sys 0.59.0", ] [[package]] @@ -81,12 +96,45 @@ dependencies = [ "num-traits", ] +[[package]] +name = "atomic-waker" +version = "1.1.2" +source = 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Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\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/1855](https://doi.org/10.3334/ORNLDAAC/1855) 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#monthly).\n","cube:dimensions":{"x":{"axis":"x","step":1000.0,"type":"spatial","extent":[-4560250.0,3252750.0],"description":"x coordinate of projection","reference_system":{"name":"undefined","type":"ProjectedCRS","$schema":"https://proj.org/schemas/v0.4/projjson.schema.json","base_crs":{"name":"undefined","datum":{"name":"undefined","type":"GeodeticReferenceFrame","ellipsoid":{"name":"undefined","semi_major_axis":6378137,"inverse_flattening":298.257223563}},"coordinate_system":{"axis":[{"name":"Longitude","unit":"degree","direction":"east","abbreviation":"lon"},{"name":"Latitude","unit":"degree","direction":"north","abbreviation":"lat"}],"subtype":"ellipsoidal"}},"conversion":{"name":"unknown","method":{"id":{"code":9802,"authority":"EPSG"},"name":"Lambert Conic Conformal (2SP)"},"parameters":[{"id":{"code":8823,"authority":"EPSG"},"name":"Latitude of 1st standard parallel","unit":"degree","value":25},{"id":{"code":8824,"authority":"EPSG"},"name":"Latitude of 2nd standard parallel","unit":"degree","value":60},{"id":{"code":8821,"authority":"EPSG"},"name":"Latitude of false origin","unit":"degree","value":42.5},{"id":{"code":8822,"authority":"EPSG"},"name":"Longitude of false origin","unit":"degree","value":-100},{"id":{"code":8826,"authority":"EPSG"},"name":"Easting at false origin","unit":"metre","value":0},{"id":{"code":8827,"authority":"EPSG"},"name":"Northing at false origin","unit":"metre","value":0}]},"coordinate_system":{"axis":[{"name":"Easting","unit":"metre","direction":"east","abbreviation":"E"},{"name":"Northing","unit":"metre","direction":"north","abbreviation":"N"}],"subtype":"Cartesian"}}},"y":{"axis":"y","step":-1000.0,"type":"spatial","extent":[-3090000.0,4984000.0],"description":"y coordinate of projection","reference_system":{"name":"undefined","type":"ProjectedCRS","$schema":"https://proj.org/schemas/v0.4/projjson.schema.json","base_crs":{"name":"undefined","datum":{"name":"undefined","type":"GeodeticReferenceFrame","ellipsoid":{"name":"undefined","semi_major_axis":6378137,"inverse_flattening":298.257223563}},"coordinate_system":{"axis":[{"name":"Longitude","unit":"degree","direction":"east","abbreviation":"lon"},{"name":"Latitude","unit":"degree","direction":"north","abbreviation":"lat"}],"subtype":"ellipsoidal"}},"conversion":{"name":"unknown","method":{"id":{"code":9802,"authority":"EPSG"},"name":"Lambert Conic Conformal (2SP)"},"parameters":[{"id":{"code":8823,"authority":"EPSG"},"name":"Latitude of 1st standard parallel","unit":"degree","value":25},{"id":{"code":8824,"authority":"EPSG"},"name":"Latitude of 2nd standard parallel","unit":"degree","value":60},{"id":{"code":8821,"authority":"EPSG"},"name":"Latitude of false origin","unit":"degree","value":42.5},{"id":{"code":8822,"authority":"EPSG"},"name":"Longitude of false origin","unit":"degree","value":-100},{"id":{"code":8826,"authority":"EPSG"},"name":"Easting at false origin","unit":"metre","value":0},{"id":{"code":8827,"authority":"EPSG"},"name":"Northing at false origin","unit":"metre","value":0}]},"coordinate_system":{"axis":[{"name":"Easting","unit":"metre","direction":"east","abbreviation":"E"},{"name":"Northing","unit":"metre","direction":"north","abbreviation":"N"}],"subtype":"Cartesian"}}},"nv":{"type":"count","values":[0,1],"description":"Size of the 'time_bnds' variable."},"time":{"type":"temporal","extent":["1980-01-16T12:00:00Z","2020-12-16T00:00:00Z"],"description":"24-hour day based on local time"}},"sci:citation":"Thornton, M.M., R. 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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. 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NULL values are presented where data are incomplete or not available."},{"name":"tk50_100a","type":"float","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."},{"name":"tk100_150a","type":"float","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."},{"name":"tk150_999a","type":"float","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."},{"name":"tk0_20a","type":"float","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."},{"name":"tk0_30a","type":"float","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."},{"name":"tk0_100a","type":"float","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."},{"name":"tk0_150a","type":"float","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."},{"name":"tk0_999a","type":"float","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."},{"name":"musumcpcta","type":"int32","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."},{"name":"soc0_5","type":"float","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."},{"name":"soc5_20","type":"float","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."},{"name":"soc20_50","type":"float","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."},{"name":"soc50_100","type":"float","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."},{"name":"soc100_150","type":"float","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."},{"name":"soc150_999","type":"float","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."},{"name":"soc0_20","type":"float","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."},{"name":"soc0_30","type":"float","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."},{"name":"soc0_100","type":"float","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."},{"name":"soc0_150","type":"float","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."},{"name":"soc0_999","type":"float","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."},{"name":"tk0_5s","type":"float","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."},{"name":"tk5_20s","type":"float","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."},{"name":"tk20_50s","type":"float","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."},{"name":"tk50_100s","type":"float","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."},{"name":"tk100_150s","type":"float","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."},{"name":"tk150_999s","type":"float","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."},{"name":"tk0_20s","type":"float","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."},{"name":"tk0_30s","type":"float","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."},{"name":"tk0_100s","type":"float","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."},{"name":"tk0_150s","type":"float","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."},{"name":"tk0_999s","type":"float","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."},{"name":"musumcpcts","type":"int32","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."},{"name":"nccpi3corn","type":"float","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."},{"name":"nccpi3soy","type":"float","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."},{"name":"nccpi3cot","type":"float","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."},{"name":"nccpi3sg","type":"float","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."},{"name":"nccpi3all","type":"float","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."},{"name":"pctearthmc","type":"int32","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."},{"name":"rootznemc","type":"int32","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."},{"name":"rootznaws","type":"int32","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."},{"name":"droughty","type":"int32","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'"},{"name":"pwsl1pomu","type":"int32","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."},{"name":"musumcpct","type":"int32","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."},{"name":"mukey","type":"int64","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 Mapunit table and the valu1 table to the MapUnitRaster_10m raster map layer to map valu1 themes."}]},"stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"collection":"gnatsgo-tables","id":"valu1","assets":{"data":{"href":"abfs://gnatsgo/july2020/parquet/valu1.parquet","title":"Valu1","type":"application/x-parquet","roles":["data"],"table:storage_options":{"account_name":"soils"}}},"stac_version":"1.0.0"},"extent":{"spatial":{"bbox":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},"temporal":{"interval":[["2020-07-01T00:00:00Z","2020-07-01T00:00:00Z"]]}},"description":"This collection 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)","providers":[{"url":"https://www.nrcs.usda.gov/","name":"United States Department of Agriculture, Natural Resources Conservation Service","roles":["licensor","producer","processor","host"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-tables.png","type":"image/png","roles":["thumbnail"],"title":"gNATSGO"},"geoparquet-items":{"href":"abfs://items/gnatsgo-tables.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"}}},"msft:group_id":"gnatsgo","license":"CC0-1.0","id":"gnatsgo-tables","msft:container":"gnatsgo-stac","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"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.","title":"gNATSGO Soil Database - Tables","keywords":["Soils","NATSGO","SSURGO","STATSGO2","RSS","USDA","United States"],"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. 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[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]()\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual]()\n - [WorldCover 2021 v200 Validaton Report]()\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","msft:storage_account":"ai4edataeuwest","keywords":["Global","Land Cover","Sentinel","ESA"],"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"]},"id":"esa-worldcover","msft:region":"westeurope","title":"ESA WorldCover","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:container":"esa-worldcover"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/daymet-annual-na","rel":"self","type":"application/json"},{"href":"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy","rel":"license","title":"EOSDIS Data Use Policy"},{"href":"https://doi.org/10.3334/ORNLDAAC/1852","rel":"cite-as"},{"href":"https://planetarycomputer.microsoft.com/dataset/daymet-annual-na","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":null,"stac_version":"1.0.0","sci:citation":"Thornton, M.M., R. 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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_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"title":"NOAA US Gridded Climate Normals 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Near-Surface Air Temperature"},"sfcWind":{"type":"application/netcdf","roles":["data"],"title":"Daily-Mean Near-Surface Wind Speed","description":"Daily-Mean Near-Surface Wind Speed"}},"description":"The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the 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.","title":"Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)","cube:dimensions":{"lat":{"axis":"y","step":0.25,"type":"spatial","extent":[-59.875,89.875],"description":"latitude","reference_system":4326},"lon":{"axis":"x","step":0.25,"type":"spatial","extent":[0.125,359.875],"description":"longitude","reference_system":4326},"time":{"step":"P1DT0H0M0S","type":"temporal","extent":["1950-01-01T12:00:00Z","2100-12-31T00:00:00Z"],"description":"time"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"keywords":["CMIP6","NASA","Climate","Humidity","Precipitation","Temperature"],"providers":[{"url":"https://www.nasa.gov/nex","name":"NASA NEX","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host","processor"]}],"id":"nasa-nex-gddp-cmip6","stac_extensions":["https://stac-extensions.github.io/datacube/v2.0.0/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","https://stac-extensions.github.io/table/v1.2.0/schema.json"],"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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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. 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Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).","sci:publications":[{"doi":"10.5067/MODIS/MOD21A2.061","citation":"Hulley, G., & Hook, S. (2021). MODIS/Terra Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061 [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). 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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. 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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","stac_version":"1.0.0","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:region":"westeurope","msft:short_description":"3DEP Lidar collection for Cloud Optimized Geotiffs (COGs) created according to PDAL's Intensity dimension","msft:storage_account":"usgslidareuwest","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"}},"title":"USGS 3DEP Lidar Classification","license":"proprietary","msft:group_id":"3dep-lidar","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"]}],"keywords":["USGS","3DEP","COG","Classification"],"summaries":{"gsd":[5.0]}},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/kaza-hydroforecast","rel":"self","type":"application/json"},{"href":"https://cdla.dev/sharing-1-0/","rel":"license","type":"text/html","title":"Community Data License Agreement – Sharing, Version 1.0"},{"href":"https://planetarycomputer.microsoft.com/dataset/kaza-hydroforecast","rel":"describedby","type":"text/html","title":"Human readable dataset overview and reference"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast","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/collections/kaza-hydroforecast/items/zambezi-at-chavuma","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"assets":{"data":{"href":"abfs://hydroforecast/hydroforecast_seasonal_zambezi_at_chavuma.parquet","table:storage_options":{"account_name":"ai4edataeuwest"},"type":"application/x-parquet","roles":["data"],"title":"Forecast Data"}},"bbox":[22.675487,-13.08062,22.675487,-13.08062],"stac_version":"1.0.0","collection":"kaza-hydroforecast","geometry":{"type":"Point","coordinates":[22.675487,-13.08062]},"id":"zambezi-at-chavuma","properties":{"title":"Zambezi At Chavuma","datetime":null,"description":"Streamflow Forecasts for Zambezi At Chavuma","end_datetime":"2022-12-16T00:00:00+00:00","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."}],"start_datetime":"2022-01-01T00:00:00+00:00","table:row_count":3240},"stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"]},"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.","stac_version":"1.0.0","msft:region":"westeurope","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"}},"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."}],"id":"kaza-hydroforecast","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]]}},"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"}},"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.","msft:storage_account":"ai4edataeuwest","providers":[{"url":"https://www.upstream.tech","name":"Upstream Tech","roles":["producer","processor","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"license":"CDLA-Sharing-1.0","msft:container":"hydroforecast","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"keywords":["Water","HydroForecast","Streamflow","Hydrology","Upstream Tech"]},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/planet-nicfi-visual","rel":"self","type":"application/json"},{"href":"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf","rel":"license","type":"application/pdf","title":"Participant License Agreement."},{"href":"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf","rel":"documentation","title":"Participant License Agreement.","type":"application/pdf"},{"href":"https://www.planet.com/nicfi/","rel":"documentation","title":"NICFI Program - Satellite Imagery and Monitoring | Planet","type":"text/html"},{"href":"https://developers.planet.com/nicfi/","rel":"documentation","type":"text/html","title":"NICFI Program Resource Center"},{"href":"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-visual","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual","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/collections/planet-nicfi-visual/items/c2156297-623d-4793-a91e-fb317c9fef04-746-1013","rel":"self","type":"application/geo+json"},{"href":"https://api.planet.com/basemaps/v1/mosaics/c2156297-623d-4793-a91e-fb317c9fef04/quads/746-1013","rel":"via","type":"application/json","title":"Planet Item"},{"href":"https://api.planet.com/basemaps/v1/mosaics/c2156297-623d-4793-a91e-fb317c9fef04","rel":"via","title":"Planet Mosaic","type":"application/json"}],"children":[],"item":null,"properties":{"gsd":4.77,"datetime":"2022-04-16T00:00:00Z","proj:bbox":[-5439870.428237628,-215246.67161490247,-5420302.548999341,-195678.7923766151],"proj:epsg":3857,"proj:shape":[4096,4096],"end_datetime":"2022-05-01T00:00:00.000Z","proj:geometry":{"type":"Polygon","coordinates":[[[-5439870.428237628,-215246.67161490247],[-5420302.548999341,-215246.67161490247],[-5420302.548999341,-195678.7923766151],[-5439870.428237628,-195678.7923766151],[-5439870.428237628,-215246.67161490247]]]},"proj:transform":[4.77731426716,0.0,-5439870.428237628,0.0,-4.77731426716,-195678.7923766151,0.0,0.0,1.0],"start_datetime":"2022-04-01T00:00:00.000Z","planet-nicfi:cadence":"monthly"},"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/eo/v1.0.0/schema.json"],"geometry":{"type":"Polygon","coordinates":[[[-48.8671875,-1.9332268],[-48.6914062,-1.9332268],[-48.6914062,-1.7575368],[-48.8671875,-1.7575368],[-48.8671875,-1.9332268]]]},"id":"c2156297-623d-4793-a91e-fb317c9fef04-746-1013","assets":{"data":{"href":"https://planet.blob.core.windows.net/nicfi/visual/c2156297-623d-4793-a91e-fb317c9fef04/746-1013/data.tif","eo:bands":[{"name":"Red","common_name":"red"},{"name":"Green","common_name":"green"},{"name":"Blue","common_name":"blue"}],"raster:bands":[{"scale":1.0,"offset":0.0,"sampling":"area","data_type":"uint8","histogram":{"max":255.0,"min":0.0,"count":11,"buckets":[392093,458955,124074,41056,17827,7393,3312,1446,661,1759]},"statistics":{"mean":37.22133159637451,"stddev":26.024237517311953,"maximum":255,"minimum":0,"valid_percent":100.0}},{"scale":1.0,"offset":0.0,"sampling":"area","data_type":"uint8","histogram":{"max":255.0,"min":0.0,"count":11,"buckets":[49942,610702,303386,59165,15812,5045,1853,747,285,1639]},"statistics":{"mean":49.562055587768555,"stddev":21.244291100630015,"maximum":255,"minimum":0,"valid_percent":100.0}},{"scale":1.0,"offset":0.0,"sampling":"area","data_type":"uint8","histogram":{"max":255.0,"min":0.0,"count":11,"buckets":[859938,134421,34736,10808,4207,1659,802,279,155,1571]},"statistics":{"mean":16.152260780334473,"stddev":20.466290123758547,"maximum":255,"minimum":0,"valid_percent":100.0}},{"scale":1.0,"offset":0.0,"sampling":"area","data_type":"uint8","histogram":{"max":255.5,"min":254.5,"count":11,"buckets":[0,0,0,0,0,1048576,0,0,0,0]},"statistics":{"mean":255.0,"stddev":0.0,"maximum":255,"minimum":255,"valid_percent":100.0}}],"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"]},"thumbnail":{"href":"https://planet.blob.core.windows.net/nicfi/visual/c2156297-623d-4793-a91e-fb317c9fef04/746-1013/thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"Thumbnail"}},"bbox":[-48.86718749,-1.93322683,-48.69140624,-1.75753681],"collection":"planet-nicfi-visual","stac_version":"1.0.0"},"msft:storage_account":"planet","title":"Planet-NICFI Basemaps (Visual)","license":"proprietary","summaries":{"gsd":[4.77],"eo:bands":[{"name":"Red","common_name":"red","description":"visible red"},{"name":"Green","common_name":"green","description":"visible green"},{"name":"Blue","common_name":"blue","description":"visible blue"}],"planet-nicfi:cadence":["biannual","monthly"],"planet-nicfi:percent_covered":{"maximum":100,"minimum":0}},"stac_version":"1.0.0","msft:group_id":"planet-nicfi","msft:container":"nicfi","description":"*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). 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. 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.","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-visual-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"thumbnail"},"geoparquet-items":{"href":"abfs://items/planet-nicfi-visual.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"}}},"keywords":["Planet","NICFI","Satellite","Tropics","Imagery"],"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"}},"providers":[{"url":"http://planet.com","name":"Planet","roles":["processor","producer","licensor"],"description":"Contact Planet at [planet.com/contact-sales](https://www.planet.com/contact-sales/)"},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"msft:short_description":"Planet's high-resolution, analysis-ready mosaics of the world's 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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)","msft:storage_account":"rhgeuwest","id":"cil-gdpcir-cc0","keywords":["CMIP6","Climate Impact Lab","Rhodium Group","Precipitation","Temperature"],"msft:region":"westeurope","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"}},"license":"CC0-1.0","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","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"title":"CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)","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"]},"assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png","type":"image/png","title":"Thumbnail"}},"msft:short_description":"Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CC0-1.0)","msft:group_id":"cil-gdpcir","providers":[{"url":"https://impactlab.org/","name":"Climate Impact Lab","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}]},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-daily-na/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/daymet-daily-na","rel":"self","type":"application/json"},{"href":"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy","rel":"license","title":"EOSDIS Data Use Policy"},{"href":"https://doi.org/10.3334/ORNLDAAC/1840","rel":"cite-as"},{"href":"https://planetarycomputer.microsoft.com/dataset/daymet-daily-na","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":null,"msft:storage_account":"daymeteuwest","description":"Gridded estimates of daily weather parameters. [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","msft:region":"westeurope","license":"proprietary","stac_version":"1.0.0","keywords":["Daymet","North America","Temperature","Precipitation","Vapor Pressure","Weather"],"providers":[{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]},{"url":"https://doi.org/10.3334/ORNLDAAC/1840","name":"ORNL DAAC","roles":["producer"]}],"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":[14965,8075,7814],"chunks":[365,284,584],"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":[8075,7814],"chunks":[284,584],"dimensions":["y","x"],"description":"latitude coordinate"},"lon":{"type":"auxiliary","unit":"degrees_east","attrs":{"units":"degrees_east","long_name":"longitude coordinate","standard_name":"longitude"},"shape":[8075,7814],"chunks":[284,584],"dimensions":["y","x"],"description":"longitude coordinate"},"swe":{"type":"data","unit":"kg/m2","attrs":{"units":"kg/m2","long_name":"snow water equivalent","cell_methods":"area: mean time: mean","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"snow water equivalent"},"dayl":{"type":"data","unit":"s","attrs":{"units":"s","long_name":"daylength","cell_methods":"area: mean","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"daylength"},"prcp":{"type":"data","unit":"mm/day","attrs":{"units":"mm/day","long_name":"daily total precipitation","cell_methods":"area: mean time: sum","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"daily total precipitation"},"srad":{"type":"data","unit":"W/m2","attrs":{"units":"W/m2","long_name":"daylight average incident shortwave radiation","cell_methods":"area: mean time: mean","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"daylight average incident shortwave radiation"},"tmax":{"type":"data","unit":"degrees C","attrs":{"units":"degrees C","long_name":"daily maximum temperature","cell_methods":"area: mean time: maximum","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"daily maximum temperature"},"tmin":{"type":"data","unit":"degrees C","attrs":{"units":"degrees C","long_name":"daily minimum temperature","cell_methods":"area: mean time: minimum","grid_mapping":"lambert_conformal_conic"},"shape":[14965,8075,7814],"chunks":[365,284,584],"dimensions":["time","y","x"],"description":"daily minimum temperature"},"yearday":{"type":"data","attrs":{"long_name":"day of year (DOY) starting with day 1 on January 1st"},"shape":[14965],"chunks":[365],"dimensions":["time"],"description":"day of year (DOY) starting with day 1 on January 1st"},"time_bnds":{"type":"data","attrs":{},"shape":[14965,2],"chunks":[365,2],"dimensions":["time","nv"]},"lambert_conformal_conic":{"type":"data","attrs":{"false_easting":0.0,"false_northing":0.0,"semi_major_axis":6378137.0,"grid_mapping_name":"lambert_conformal_conic","standard_parallel":[25.0,60.0],"inverse_flattening":298.257223563,"latitude_of_projection_origin":42.5,"longitude_of_central_meridian":-100.0},"shape":[],"dimensions":[]}},"msft:short_description":"Daily surface weather data on a 1-km grid for North America","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. 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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 - Band 01 (full resolution)","eo:bands":[{"name":"ABI Band 1","common_name":"blue","description":"Daytime aerosol over land, coastal water mapping","center_wavelength":0.47}]},"C01_2km":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","title":"Cloud and Moisture Imagery reflectance factor - Band 01","eo:bands":[{"name":"ABI Band 1","common_name":"blue","description":"Daytime aerosol over land, coastal water mapping","center_wavelength":0.47}]},"C02_2km":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","title":"Cloud and Moisture Imagery reflectance factor - Band 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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","Crowleys 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","Crowleys 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 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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 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States"],"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 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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 Marsh"},{"values":[402],"summary":"Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh"},{"values":[403],"summary":"Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh"},{"values":[404],"summary":"Florida Big Bend Fresh-Oligohaline Tidal Marsh"},{"values":[405],"summary":"Atlantic Coastal Plain Depression Pondshore"},{"values":[406],"summary":"Atlantic Coastal Plain Large Natural Lakeshore"},{"values":[407],"summary":"Central Florida Herbaceous Pondshore"},{"values":[408],"summary":"Central Florida Herbaceous Seep"},{"values":[409],"summary":"East Gulf Coastal Plain Savanna and Wet Prairie"},{"values":[410],"summary":"East Gulf Coastal Plain Depression Pondshore"},{"values":[411],"summary":"Floridian Highlands Freshwater Marsh"},{"values":[412],"summary":"Southern Coastal Plain Herbaceous Seepage Bog"},{"values":[413],"summary":"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier"},{"values":[414],"summary":"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier"},{"values":[415],"summary":"Texas-Louisiana Coastal Prairie Slough"},{"values":[416],"summary":"Central Interior and Appalachian Shrub-Herbaceous Wetland Systems"},{"values":[417],"summary":"Great Lakes Coastal Marsh Systems"},{"values":[418],"summary":"0"},{"values":[419],"summary":"0"},{"values":[420],"summary":"Laurentian-Acadian Shrub-Herbaceous Wetland Systems"},{"values":[421],"summary":"0"},{"values":[422],"summary":"Eastern Great Plains Wet Meadow, Prairie and Marsh"},{"values":[423],"summary":"Great Lakes Wet-Mesic Lakeplain Prairie"},{"values":[424],"summary":"Great Plains Prairie Pothole"},{"values":[425],"summary":"Western Great Plains Closed Depression Wetland"},{"values":[426],"summary":"Western Great Plains Depressional Wetland Systems"},{"values":[427],"summary":"Western Great Plains Open Freshwater Depression Wetland"},{"values":[428],"summary":"Cumberland Riverscour"},{"values":[429],"summary":"Inter-Mountain Basins Interdunal Swale Wetland"},{"values":[430],"summary":"North Pacific Avalanche Chute Shrubland"},{"values":[431],"summary":"North Pacific Intertidal Freshwater Wetland"},{"values":[432],"summary":"Temperate Pacific Freshwater Emergent Marsh"},{"values":[433],"summary":"Temperate Pacific Freshwater Mudflat"},{"values":[434],"summary":"Columbia Plateau Vernal Pool"},{"values":[435],"summary":"Northern California Claypan Vernal Pool"},{"values":[436],"summary":"Northern Rocky Mountain Wooded Vernal Pool"},{"values":[437],"summary":"Columbia Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe"},{"values":[438],"summary":"Rocky Mountain Alpine-Montane Wet Meadow"},{"values":[439],"summary":"Rocky Mountain Subalpine-Montane Riparian Shrubland"},{"values":[440],"summary":"Temperate Pacific Montane Wet Meadow"},{"values":[441],"summary":"Willamette Valley Wet Prairie"},{"values":[442],"summary":"Chihuahuan-Sonoran Desert Bottomland and Swale Grassland"},{"values":[443],"summary":"North American Arid West Emergent Marsh"},{"values":[444],"summary":"North American Warm Desert Riparian Mesquite Bosque"},{"values":[445],"summary":"Western Great Plains Saline Depression Wetland"},{"values":[446],"summary":"Acadian Salt Marsh and Estuary Systems"},{"values":[447],"summary":"Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh"},{"values":[448],"summary":"Atlantic Coastal Plain Embayed Region Tidal Salt and Brackish Marsh"},{"values":[449],"summary":"Atlantic Coastal Plain Indian River Lagoon Tidal Marsh"},{"values":[450],"summary":"Atlantic Coastal Plain Northern Tidal Salt Marsh"},{"values":[451],"summary":"Florida Big Bend Salt-Brackish Tidal Marsh"},{"values":[452],"summary":"Gulf and Atlantic Coastal Plain Tidal Marsh Systems"},{"values":[453],"summary":"Mississippi Sound Salt and Brackish Tidal Marsh"},{"values":[454],"summary":"Texas Saline Coastal Prairie"},{"values":[455],"summary":"Temperate Pacific Tidal Salt and Brackish Marsh"},{"values":[456],"summary":"Inter-Mountain Basins Alkaline Closed Depression"},{"values":[457],"summary":"Inter-Mountain Basins Greasewood Flat"},{"values":[458],"summary":"Inter-Mountain Basins Playa"},{"values":[459],"summary":"North American Warm Desert Playa"},{"values":[460],"summary":"Apacherian-Chihuahuan Mesquite Upland Scrub"},{"values":[461],"summary":"Apacherian-Chihuahuan Semi-Desert Grassland and Steppe"},{"values":[462],"summary":"Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub"},{"values":[463],"summary":"Chihuahuan Gypsophilous Grassland and Steppe"},{"values":[464],"summary":"Chihuahuan Loamy Plains Desert Grassland"},{"values":[465],"summary":"Chihuahuan Mixed Desert and Thorn Scrub"},{"values":[466],"summary":"Chihuahuan Sandy Plains Semi-Desert Grassland"},{"values":[467],"summary":"Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub"},{"values":[468],"summary":"Chihuahuan Succulent Desert Scrub"},{"values":[469],"summary":"Madrean Juniper Savanna"},{"values":[470],"summary":"Mojave Mid-Elevation Mixed Desert Scrub"},{"values":[471],"summary":"North American Warm Desert Active and Stabilized Dune"},{"values":[472],"summary":"Sonora-Mojave Creosotebush-White Bursage Desert Scrub"},{"values":[473],"summary":"Sonoran Mid-Elevation Desert Scrub"},{"values":[474],"summary":"Sonoran Paloverde-Mixed Cacti Desert Scrub"},{"values":[475],"summary":"Chihuahuan Mixed Salt Desert Scrub"},{"values":[476],"summary":"Sonora-Mojave Mixed Salt Desert Scrub"},{"values":[477],"summary":"North American Warm Desert Wash"},{"values":[478],"summary":"South Texas Lomas"},{"values":[479],"summary":"Tamaulipan Calcareous Thornscrub"},{"values":[480],"summary":"Tamaulipan Clay Grassland"},{"values":[481],"summary":"Tamaulipan Mesquite Upland Scrub"},{"values":[482],"summary":"Tamaulipan Mixed Deciduous Thornscrub"},{"values":[483],"summary":"Tamaulipan Savanna Grassland"},{"values":[484],"summary":"Inter-Mountain Basins Mat Saltbush Shrubland"},{"values":[485],"summary":"Inter-Mountain Basins Mixed Salt Desert Scrub"},{"values":[486],"summary":"Inter-Mountain Basins Wash"},{"values":[487],"summary":"Columbia Plateau Steppe and Grassland"},{"values":[488],"summary":"Great Basin Xeric Mixed Sagebrush Shrubland"},{"values":[489],"summary":"Inter-Mountain Basins Big Sagebrush Shrubland"},{"values":[490],"summary":"Inter-Mountain Basins Big Sagebrush Steppe"},{"values":[491],"summary":"Inter-Mountain Basins Montane Sagebrush Steppe"},{"values":[492],"summary":"Colorado Plateau Mixed Low Sagebrush Shrubland"},{"values":[493],"summary":"Columbia Plateau Low Sagebrush Steppe"},{"values":[494],"summary":"Columbia Plateau Scabland Shrubland"},{"values":[495],"summary":"Wyoming Basins Dwarf Sagebrush Shrubland and Steppe"},{"values":[496],"summary":"Colorado Plateau Blackbrush-Mormon-tea Shrubland"},{"values":[497],"summary":"Inter-Mountain Basins Semi-Desert Grassland"},{"values":[498],"summary":"Inter-Mountain Basins Semi-Desert Shrub Steppe"},{"values":[499],"summary":"Southern Colorado Plateau Sand Shrubland"},{"values":[500],"summary":"Acadian-Appalachian Alpine Tundra"},{"values":[501],"summary":"Rocky Mountain Alpine Dwarf-Shrubland"},{"values":[502],"summary":"Rocky Mountain Alpine Fell-Field"},{"values":[503],"summary":"Rocky Mountain Alpine Turf"},{"values":[504],"summary":"Mediterranean California Alpine Dry Tundra"},{"values":[505],"summary":"Mediterranean California Alpine Fell-Field"},{"values":[506],"summary":"North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow"},{"values":[507],"summary":"Rocky Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit"},{"values":[508],"summary":"Temperate Pacific Intertidal Mudflat"},{"values":[509],"summary":"Mediterranean California Eelgrass Bed"},{"values":[510],"summary":"North Pacific Maritime Eelgrass Bed"},{"values":[511],"summary":"South-Central Interior Large Floodplain - Herbaceous Modifier"},{"values":[512],"summary":"East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous Modifier"},{"values":[513],"summary":"Temperate Pacific Freshwater Aquatic Bed"},{"values":[514],"summary":"Central California Coast Ranges Cliff and Canyon"},{"values":[515],"summary":"Mediterranean California Serpentine Barrens"},{"values":[516],"summary":"Southern California Coast Ranges Cliff and Canyon"},{"values":[517],"summary":"Central Interior Acidic Cliff and Talus"},{"values":[518],"summary":"Central Interior Calcareous Cliff and Talus"},{"values":[519],"summary":"East Gulf Coastal Plain Dry Chalk Bluff"},{"values":[520],"summary":"North-Central Appalachian Acidic Cliff and Talus"},{"values":[521],"summary":"North-Central Appalachian Circumneutral Cliff and Talus"},{"values":[522],"summary":"Southern Appalachian Montane Cliff"},{"values":[523],"summary":"Southern Interior Acid Cliff"},{"values":[524],"summary":"Southern Interior Calcareous Cliff"},{"values":[525],"summary":"Southern Piedmont Cliff"},{"values":[526],"summary":"Southern Appalachian Granitic Dome"},{"values":[527],"summary":"Southern Appalachian Rocky Summit"},{"values":[528],"summary":"Southern Piedmont Granite Flatrock"},{"values":[529],"summary":"Rocky Mountain Cliff, Canyon and Massive Bedrock"},{"values":[530],"summary":"Klamath-Siskiyou Cliff and Outcrop"},{"values":[531],"summary":"North Pacific Montane Massive Bedrock, Cliff and Talus"},{"values":[532],"summary":"North Pacific Serpentine Barren"},{"values":[533],"summary":"North Pacific Active Volcanic Rock and Cinder Land"},{"values":[534],"summary":"Sierra Nevada Cliff and Canyon"},{"values":[535],"summary":"Western Great Plains Badland"},{"values":[536],"summary":"Southwestern Great Plains Canyon"},{"values":[537],"summary":"Western Great Plains Cliff and Outcrop"},{"values":[538],"summary":"North American Warm Desert Badland"},{"values":[539],"summary":"North American Warm Desert Bedrock Cliff and Outcrop"},{"values":[540],"summary":"North American Warm Desert Pavement"},{"values":[541],"summary":"North American Warm Desert Volcanic Rockland"},{"values":[542],"summary":"Colorado Plateau Mixed Bedrock Canyon and Tableland"},{"values":[543],"summary":"Columbia Plateau Ash and Tuff Badland"},{"values":[544],"summary":"Geysers and Hot Springs"},{"values":[545],"summary":"Inter-Mountain Basins Active and Stabilized Dune"},{"values":[546],"summary":"Inter-Mountain Basins Cliff and Canyon"},{"values":[547],"summary":"Inter-Mountain Basins Shale Badland"},{"values":[548],"summary":"Inter-Mountain Basins Volcanic Rock and Cinder Land"},{"values":[549],"summary":"Rocky Mountain Alpine Bedrock and Scree"},{"values":[550],"summary":"Mediterranean California Alpine Bedrock and Scree"},{"values":[551],"summary":"North Pacific Alpine and Subalpine Bedrock and Scree"},{"values":[552],"summary":"Unconsolidated Shore"},{"values":[553],"summary":"Undifferentiated Barren Land"},{"values":[554],"summary":"North American Alpine Ice Field"},{"values":[555],"summary":"Orchards Vineyards and Other High Structure Agriculture"},{"values":[556],"summary":"Cultivated Cropland"},{"values":[557],"summary":"Pasture/Hay"},{"values":[558],"summary":"Introduced Upland Vegetation - Annual Grassland"},{"values":[559],"summary":"Introduced Upland Vegetation - Perennial Grassland and Forbland"},{"values":[560],"summary":"Modified/Managed Southern Tall Grassland"},{"values":[561],"summary":"Introduced Upland Vegetation - Shrub"},{"values":[562],"summary":"Introduced Riparian and Wetland Vegetation"},{"values":[563],"summary":"Introduced Upland Vegetation - Treed"},{"values":[564],"summary":"0"},{"values":[565],"summary":"Disturbed, Non-specific"},{"values":[566],"summary":"Recently Logged Areas"},{"values":[567],"summary":"Harvested Forest - Grass/Forb Regeneration"},{"values":[568],"summary":"Harvested Forest-Shrub Regeneration"},{"values":[569],"summary":"Harvested Forest - Northwestern Conifer Regeneration"},{"values":[570],"summary":"Recently Burned"},{"values":[571],"summary":"Recently burned grassland"},{"values":[572],"summary":"Recently burned shrubland"},{"values":[573],"summary":"Recently burned forest"},{"values":[574],"summary":"Disturbed/Successional - Grass/Forb Regeneration"},{"values":[575],"summary":"Disturbed/Successional - Shrub Regeneration"},{"values":[576],"summary":"Disturbed/Successional - Recently Chained Pinyon-Juniper"},{"values":[577],"summary":"Open Water (Aquaculture)"},{"values":[578],"summary":"Open Water (Brackish/Salt)"},{"values":[579],"summary":"Open Water (Fresh)"},{"values":[580],"summary":"Quarries, Mines, Gravel Pits and Oil Wells"},{"values":[581],"summary":"Developed, Open Space"},{"values":[582],"summary":"Developed, Low Intensity"},{"values":[583],"summary":"Developed, Medium Intensity"},{"values":[584],"summary":"Developed, High Intensity"}]}},"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/label/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"],"license":"proprietary","id":"gap","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","msft:region":"westeurope","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 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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"]}],"msft:short_description":"ESA CCI global land cover maps in NetCDF format","msft:region":"westeurope","stac_version":"1.0.0","id":"esa-cci-lc-netcdf","keywords":["Land Cover","ESA","CCI","Global"],"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.","summaries":{"esa_cci_lc:version":["2.0.7cds","2.1.1"]},"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","msft:group_id":"esa-cci-lc","item_assets":{"netcdf":{"type":"application/netcdf","roles":["data","quality"],"title":"ESA CCI Land Cover NetCDF 4 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measurement flag"},{"name":"years_MLY-TMIN-AVGNDS-LSTH070","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-NORMAL","type":"double","unit":"degree Fahrenheit","description":"Long-term averages of monthly minimum temperature"},{"name":"comp_flag_MLY-TMIN-NORMAL","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-NORMAL","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-NORMAL","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH016","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 16F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH016","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH016","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH016","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH020","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 20F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH020","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH020","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH020","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH024","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 24F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH024","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH024","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH024","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH028","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 28F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH028","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH028","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH028","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH032","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 32F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH032","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH032","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH032","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-PRBOCC-LSTH036","type":"double","unit":"percent","description":"Probability of minimum temperature at least once monthly <= 36F"},{"name":"comp_flag_MLY-TMIN-PRBOCC-LSTH036","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-PRBOCC-LSTH036","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-PRBOCC-LSTH036","type":"double","description":"Number of years used"},{"name":"MLY-TMIN-STDDEV","type":"double","unit":"degree Fahrenheit","description":"Long-term standard deviations of monthly minimum temperature"},{"name":"comp_flag_MLY-TMIN-STDDEV","type":"byte_array","description":"Data record completeness flag"},{"name":"meas_flag_MLY-TMIN-STDDEV","type":"byte_array","description":"Data record measurement flag"},{"name":"years_MLY-TMIN-STDDEV","type":"double","description":"Number of years used"}],"start_datetime":"2006-01-01T00:00:00Z","table:row_count":161652,"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."}],"noaa_climate_normals:period":"2006-2020","noaa_climate_normals:frequency":"monthly"},"stac_version":"1.0.0","bbox":[-170.7136,-14.3306,171.3833,71.3214],"geometry":{"type":"Polygon","coordinates":[[[171.3833,-14.3306],[171.3833,71.3214],[-170.7136,71.3214],[-170.7136,-14.3306],[171.3833,-14.3306]]]},"collection":"noaa-climate-normals-tabular","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"assets":{"geoparquet":{"href":"abfs://climate-normals-geoparquet/2006_2020-monthly.parquet","type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"noaanormals"}}},"id":"2006_2020-monthly"},"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"]}],"msft:region":"eastus","msft:short_description":"Tabular Climate Normal data for over 15,000 United States weather station locations in GeoParquet format.","item_assets":{"geoparquet":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"noaanormals"}}},"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:group_id":"noaa-climate-normals","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","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"}}},"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"],"msft:storage_account":"noaanormals","license":"proprietary","stac_version":"1.0.0","extent":{"spatial":{"bbox":[[-177.38333,-14.3306,174.1,71.3214]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"title":"NOAA US Tabular Climate Normals","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"}],"keywords":["NOAA","Climate Normals","Weather","Surface Observations","Climatology","CONUS"],"summaries":{"noaa_climate_normals:period":["1981-2010","1991-2020","2006-2020"],"noaa_climate_normals:frequency":["hourly","daily","monthly","annualseasonal"]},"id":"noaa-climate-normals-tabular"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/fia","rel":"self","type":"application/json"},{"href":"https://www.fs.usda.gov/rds/archive/datauseinfo/open","rel":"license","type":"text/html","title":"USDA Open Access Data Use Agreement"},{"href":"https://planetarycomputer.microsoft.com/dataset/fia","rel":"describedby","type":"text/html","title":"Human readable dataset overview and reference"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia","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/collections/fia/items/tree_woodland_stems","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"properties":{"datetime":"2020-06-01T00:00:00Z","table:columns":[{"name":"CN","type":"int64","description":"Sequence number"},{"name":"PLT_CN","type":"int64","description":"Plot sequence number"},{"name":"INVYR","type":"int64","description":"Inventory year"},{"name":"STATECD","type":"int64","description":"State code"},{"name":"UNITCD","type":"int64","description":"Survey unit code"},{"name":"COUNTYCD","type":"int64","description":"County code"},{"name":"PLOT","type":"int64","description":"Plot number"},{"name":"SUBP","type":"int64","description":"Subplot number"},{"name":"TREE","type":"int64","description":"Woodland tree number"},{"name":"TRE_CN","type":"int64","description":"Tree sequence number"},{"name":"DIA","type":"double","description":"Woodland stem diameter"},{"name":"STATUSCD","type":"int64","description":"Woodland stem status code"},{"name":"STEM_NBR","type":"int64","description":"Woodland stem number"},{"name":"CYCLE","type":"int64","description":"Inventory cycle number"},{"name":"SUBCYCLE","type":"int64","description":"Inventory subcycle number"},{"name":"CREATED_BY","type":"double","description":"Created by"},{"name":"CREATED_DATE","type":"byte_array","description":"Created date"},{"name":"CREATED_IN_INSTANCE","type":"int64","description":"Created in instance"},{"name":"MODIFIED_BY","type":"double","description":"Modified by"},{"name":"MODIFIED_DATE","type":"byte_array","description":"Modified date"},{"name":"MODIFIED_IN_INSTANCE","type":"double","description":"Modified in instance"}]},"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"id":"tree_woodland_stems","geometry":{"type":"Polygon","coordinates":[[[179.77847,-14.53],[179.77847,71.352561],[-179.14734,71.352561],[-179.14734,-14.53],[179.77847,-14.53]]]},"bbox":[-179.14734,-14.53,179.77847,71.352561],"assets":{"data":{"href":"abfs://cpdata/raw/fia/tree_woodland_stems.parquet","type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"cpdataeuwest"}}},"collection":"fia"},"msft:container":"cpdata","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"]}],"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":"cpdataeuwest","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.

* 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.

* 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.

* PLOT.CN = COND.PLT_CN links the unique plot record to the condition class record(s).

* PLOT.CN = SUBPLOT.PLT_CN links the unique plot record to the subplot records.

* PLOT.CN = TREE.PLT_CN links the unique plot record to the tree records.

* 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).

* PLOT.CN = COND.PLT_CN links the condition class record(s) to the plot table.

* COND.PLT_CN = SITETREE.PLT_CN and COND.CONDID = SITETREE.CONDID links the condition class record to the site tree data.

* 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.

* PLOT.CN = SUBPLOT.PLT_CN links the unique plot record to the subplot records.

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MACRCOND = COND.CONDID links the macroplot conditions to the condition class record.

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.SUBPCOND = COND.CONDID links the subplot conditions to the condition class record.

* 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.

* PLOT.CN = SUBP_COND.PLT_CN links the subplot condition class record to the plot table.

* 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.

* PLOT.CN = SUBP_COND_CHNG_MTRX.PLT_CN links the subplot condition change matrix records to the unique plot record.

* 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.

* PLOT.CN = TREE.PLT_CN links the tree records to the unique plot record.

* 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.

* 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.

* 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.

* 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.

* 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.

* 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.

* PLOT.CN = TREE_GRM_ESTN.PLT_CN links the tree GRM estimation records to the unique plot record.

* 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.

* PLOT.CN = SEEDLING.PLT_CN links the seedling records to the unique plot record.

* 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.

* PLOT.CN = SITETREE.PLT_CN links the site tree records to the unique plot record.

* 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.

* PLOT.CN = INVASIVE_SUBPLOT_SPP.PLT_CN links the invasive subplot species record(s) to the unique plot record.

* 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.

* 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.

* PLOT.CN = P2VEG_SUBPLOT_SPP.PLT_CN links the vegetation subplot species record(s) to the unique plot record.

* 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.

* 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.

* PLOT.CN = P2VEG_SUBP_STRUCTURE. PLT_CN links the subplot structure record(s) to the unique plot record.

* 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.

* 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.

* PLOT.CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN links the down woody material coarse woody debris records to the unique plot record.

* 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.

* PLOT.CN = DWM_DUFF_LITTER_FUEL.PLT_CN links the duff, litter, fuelbed records to the unique plot record.

* 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.

* PLOT.CN = DWM_FINE_WOODY_DEBRIS.PLT_CN links the fine woody debris records to the unique plot record.

* 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.

* 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.

* PLOT.CN = DWM_RESIDUAL_PILE.PLT_CN links the wood piles records to the unique plot record.

* 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.

* PLOT.CN = DWM_TRANSECT_SEGMENT.PLT_CN links the down woody material transect length records to the unique plot record.

* 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).

* PLOT.CN = COND_DWM_CALC.PLT_CN links the down woody material calculation records to the unique plot record.

* COND.CN = COND_DWM_CALC.CND_CN links the down woody material calculation records to the unique condition record.

* 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.

* 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.

* PLOT.CN = SUBPLOT_REGEN.PLT_CN links the unique plot record to the subplot regeneration records.

* 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.

* PLOT.CN = SEEDLING_REGEN.PLT_CN links the unique plot record to the seedling regeneration records.

* 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.

* 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. If an evaluation can produce only 22 of all the population estimates in the REF_POP_ATTRIBUTE table, there will be 22 records in the POP_EVAL_ATTRIBUTE table (one per population estimate) for that evaluation.

* POP_EVAL.CN = POP_EVAL_ATTRIBUTE.EVAL_CN links the unique evaluation identifier to the list of population estimates that can be derived for that evaluation.","msft:item_name":"pop_eval_attribute"},{"name":"Population Evaluation Group Table","description":"Population evaluation group table. This table lists and describes the evaluation groups. One record in the POP_EVAL_GRP table can be linked to all the evaluations that were used in generating estimates for a State inventory report.

* POP_EVAL_GRP.CN = POP_EVAL_TYP.EVAL_GRP_CN links the evaluation group record to the evaluation type record.","msft:item_name":"pop_eval_grp"},{"name":"Population Evaluation Type Table","description":"Population evaluation type table. This table provides information on the type of evaluations that were used to generate a set of tables for an inventory report. In a typical State inventory report, one evaluation is used to generate an estimate of the total land area; a second evaluation is used to generate current estimates of volume, numbers of trees and biomass; and a third evaluation is used for estimating growth, removals and mortality.

* POP_EVAL_TYP.EVAL_CN = POP_EVAL.CN links the evaluation type record to the evaluation record.

* POP_EVAL_TYP.EVAL_GRP_CN = POP_EVAL_GRP.CN links the evaluation type record to the evaluation group record.

* POP_EVAL_TYP.EVAL_TYP = REF_POP_EVAL_TYP_DESCR.EVAL_TYP links an evaluation type record to an evaluation type description reference record.","msft:item_name":"pop_eval_typ"},{"name":"Population Plot Stratum Assignment Table","description":"Population plot stratum assignment table. This table provides a way to assign stratum information to a plot. Stratum information is assigned to a plot by overlaying the plot's location on the Phase 1 imagery. Plots are linked to their appropriate stratum for an evaluation via the POP_PLOT_STRATUM_ASSGN table.

* POP_PLOT_STRATUM_ASSGN.PLT_CN = PLOT.CN links the stratum assigned to the plot record.","msft:item_name":"pop_plot_stratum_assgn"},{"name":"Population Stratum Table","description":"Population stratum table. This table provides information about individual strata. The area within an estimation unit is divided into strata. The area for each stratum can be calculated by determining the proportion of Phase 1 pixels/plots in each stratum and multiplying that proportion by the total area in the estimation unit. Information for a single stratum is stored in a single record of the POP_STRATUM table.

* POP_STRATUM.CN = POP_PLOT_STRATUM_ASSGN.STRATUM_CN links the defined stratum to each plot.","msft:item_name":"pop_stratum"},{"name":"Plot Geometry Table","description":"Plot geometry table. This table contains geometric attributes associated with the plot location, such as the hydrological unit and roadless codes.

* PLOTGEOM.CN = PLOT.CN links the unique plot record between the two tables.","msft:item_name":"plotgeom"},{"name":"Plot Snapshot Table","description":"Plot snapshot table. This table combines the information in the PLOT table with information in the PLOT_EVAL_GRP and POP_STRATUM tables to provide a snapshot of the plot records with their associated expansion and adjustment factors.

* PLOTSNAP.CN = PLOT.CN links the unique plot record between the two tables.","msft:item_name":"plotsnap"}],"license":"CC0-1.0","id":"fia","title":"Forest Inventory and Analysis","keywords":["Forest","Species","Carbon","Biomass","USDA","Forest Service"],"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]]}},"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","msft:short_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 (FIA) program","stac_version":"1.0.0","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"}}},"msft:region":"westeurope","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"cpdataeuwest"}}}},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/naip","rel":"self","type":"application/json"},{"href":"https://www.fsa.usda.gov/help/policies-and-links/","rel":"license","title":"Public Domain"},{"href":"https://planetarycomputer.microsoft.com/dataset/naip","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip","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/collections/naip/items/pr_m_1806551_nw_20_030_20221212_20230329","rel":"self","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=naip&item=pr_m_1806551_nw_20_030_20221212_20230329","rel":"preview","type":"text/html","title":"Map of item"}],"children":[],"item":null,"stac_version":"1.0.0","collection":"naip","stac_extensions":["https://stac-extensions.github.io/eo/v1.0.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json"],"bbox":[-65.75386,18.183872,-65.683663,18.253643],"id":"pr_m_1806551_nw_20_030_20221212_20230329","geometry":{"type":"Polygon","coordinates":[[[-65.683663,18.184851],[-65.684718,18.253643],[-65.75386,18.25266],[-65.752778,18.183872],[-65.683663,18.184851]]]},"assets":{"image":{"href":"https://naipeuwest.blob.core.windows.net/naip/v002/pr/2022/pr_030cm_2022/18065/51/m_1806551_nw_20_030_20221212_20230329.tif","type":"image/tiff; 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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.","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"}}},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/era5-pds","rel":"self","type":"application/json"},{"href":"https://apps.ecmwf.int/datasets/licences/copernicus/","rel":"license","title":"License to Use Copernicus 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downwards"}},"start_datetime":"2020-12-01T00:00:00Z","cube:dimensions":{"lat":{"axis":"y","step":-0.25,"type":"spatial","extent":[-90.0,90.0],"description":"latitude","reference_system":"epsg:4326"},"lon":{"axis":"x","step":0.25,"type":"spatial","extent":[0.0,359.75],"description":"longitude","reference_system":"epsg:4326"},"time":{"step":"P0DT1H0M0S","type":"temporal","extent":["2020-12-01T00:00:00Z","2020-12-31T23:00:00Z"]}}},"stac_version":"1.0.0"},"title":"ERA5 - PDS","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).","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"stac_version":"1.0.0","msft:short_description":"A comprehensive reanalysis, which assimilates as many observations as possible in the upper air and near 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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.","stac_version":"1.0.0","msft:region":"westeurope","id":"fws-nwi","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"]},"title":"FWS National Wetlands Inventory","license":"proprietary","stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"item_assets":{"zip":{"type":"application/zip","roles":["data","archive","source"]}},"msft:storage_account":"ai4edataeuwest","keywords":["USFWS","Wetlands","United States"]},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/gbif","rel":"self","type":"application/json"},{"href":"https://www.gbif.org/terms","rel":"license","type":"text/html","title":"GBIF Terms of Use"},{"href":"https://planetarycomputer.microsoft.com/dataset/gbif","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif","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/collections/gbif/items/gbif-2024-10-01","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"geometry":{"type":"Polygon","coordinates":[[[180,-90],[180,90],[-180,90],[-180,-90],[180,-90]]]},"properties":{"datetime":"2024-10-01T00:00:00Z","table:columns":[{"name":"gbifid","type":"byte_array","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":"int64","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":"int64","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/), [`CC_BY_4_0`](https://creativecommons.org/licenses/by/4.0/), or (`CC_BY_NC_4_0`)[https://creativecommons.org/licenses/by-nc/4.0/]."},{"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":"int64","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/)."}]},"stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"id":"gbif-2024-10-01","collection":"gbif","stac_version":"1.0.0","bbox":[-180,-90,180,90],"assets":{"data":{"href":"az://gbif/occurrence/2024-10-01/occurrence.parquet","type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"ai4edataeuwest"}}}},"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"],"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"}}},"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","msft:container":"gbif","id":"gbif","msft:short_description":"Global biodiversity observation records, documenting over 1.6 billion species occurrences","msft:region":"westeurope","msft:storage_account":"ai4edataeuwest","license":"proprietary","keywords":["GBIF","Biodiversity","Species"],"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. 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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 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. 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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).","msft:storage_account":"usgslidareuwest","msft:region":"westeurope","summaries":{"gsd":[2.0]},"title":"USGS 3DEP Lidar 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"]}],"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 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reference"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm","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/collections/goes-glm/items/OR_GLM-L2-LCFA_G18_s20243020842000_e20243020842200","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"id":"OR_GLM-L2-LCFA_G18_s20243020842000_e20243020842200","assets":{"netcdf":{"href":"https://goeseuwest.blob.core.windows.net/noaa-goes18/GLM-L2-LCFA/2024/302/08/OR_GLM-L2-LCFA_G18_s20243020842000_e20243020842200_c20243020842222.nc","created":"2024-10-28T08:42:22.2Z","type":"application/netcdf","roles":["data"],"title":"Original NetCDF4 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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.","title":"GOES-R Lightning Detection","msft:group_id":"goes","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"],"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\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF 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).","license":"proprietary","sci:doi":"10.7289/V5KH0KK6","id":"goes-glm","msft:storage_account":"goeseuwest","msft:container":"noaa-goes-geoparquet","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 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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","license":"proprietary","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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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. 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10-day surface reflectance and NDVI (SYNERGY V10, a SPOT VEGETATION Continuity Product).","license":"proprietary","stac_version":"1.0.0","title":"Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)","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","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"],"extent":{"spatial":{"bbox":[[-180.0,-56.0,180.0,75.0]]},"temporal":{"interval":[["2018-09-27T11:17:21Z",null]]}},"keywords":["Sentinel","Copernicus","ESA","Satellite","Reflectance","NDVI"],"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":"Surface Reflectance Data Set associated with VGT-B0 channel"},"b2":{"type":"application/x-netcdf","roles":["data"],"eo:bands":[{"name":"B2","description":"OLCI channels Oa06, Oa07, Oa08, Oa09, 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approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. 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).","license":"CDLA-Permissive-1.0","providers":[{"url":"https://www.deltares.nl/en/","name":"Deltares","roles":["producer"]},{"url":"https://planetarycomputer.microsoft.com/","name":"Microsoft","roles":["host"]}],"summaries":{"deltares:reservoir":["ERA5","CHIRPS","EOBS","NLDAS","BOM"]},"msft:region":"westeurope","id":"deltares-water-availability","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"],"title":"Deltares Global Water 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Projections for Climate Impacts Research (CC-BY-SA-4.0)","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:container":"cil-gdpcir","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"]]}},"msft:region":"westeurope","stac_version":"1.0.0","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:short_description":"Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)","id":"cil-gdpcir-cc-by-sa","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: 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Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n","keywords":["ASTER","USGS","NASA","Satellite","Global"],"id":"aster-l1t","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"],"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"]},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-04T12:00:00Z","2006-12-31T12:00:00Z"]]}},"license":"proprietary","title":"ASTER L1T","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"]}],"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"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/ecmwf-forecast","rel":"self","type":"application/json"},{"href":"https://creativecommons.org/licenses/by/4.0/","rel":"license","title":"CC-BY-4.0 license","type":"text/html"},{"href":"https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time","rel":"documentation","title":"ECMWF Open Data (Real Time) documentation","type":"text/html"},{"href":"https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast","rel":"describedby","type":"text/html","title":"Human readable dataset overview and reference"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast","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/collections/ecmwf-forecast/items/ecmwf-2024-10-27T12-waef-ep-360h-0.40","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"stac_extensions":[],"assets":{"data":{"href":"https://ai4edataeuwest.blob.core.windows.net/ecmwf/20241027/12z/ifs/0p4-beta/waef/20241027120000-360h-waef-ep.grib2","type":"application/wmo-GRIB2","roles":["data"]},"index":{"href":"https://ai4edataeuwest.blob.core.windows.net/ecmwf/20241027/12z/ifs/0p4-beta/waef/20241027120000-360h-waef-ep.index","type":"application/x-ndjson","roles":["index"]}},"collection":"ecmwf-forecast","bbox":[-180.0,-90.0,180.0,90.0],"id":"ecmwf-2024-10-27T12-waef-ep-360h-0.40","geometry":{"type":"Polygon","coordinates":[[[180,-90],[180,90],[-180,90],[-180,-90],[180,-90]]]},"properties":{"datetime":"2024-11-11T12:00:00Z","ecmwf:step":"360h","ecmwf:type":"ep","ecmwf:stream":"waef","ecmwf:resolution":"0.40","ecmwf:forecast_datetime":"2024-11-11T12:00:00Z","ecmwf:reference_datetime":"2024-10-27T12:00:00Z"},"stac_version":"1.0.0"},"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"]},"license":"CC-BY-4.0","id":"ecmwf-forecast","extent":{"spatial":{"bbox":[[-180,90,180,-90]]},"temporal":{"interval":[[null,null]]}},"stac_extensions":["https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"],"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\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 ``, 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).","stac_version":"1.0.0","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:storage_account":"ai4edataeuwest","msft:container":"ecmwf","keywords":["ECMWF","forecast","weather"],"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"}},"msft:region":"westeurope","providers":[{"url":"https://www.ecmwf.int/","name":"ECMWF","roles":["producer"]}],"msft:short_description":"ECMWF Open Data (Real Time) forecasts"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2HGF-061/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/modis-17A2HGF-061","rel":"self","type":"application/json"},{"href":"https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf","rel":"help","title":"MOD17 User Guide"},{"href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A2HGF","rel":"describedby","title":"MOD17A2HGF file specification"},{"href":"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A2HGF","rel":"describedby","title":"MYD17A2HGF file specification"},{"href":"https://doi.org/10.5067/MODIS/MOD17A2HGF.061","rel":"cite-as","title":"LP DAAC - MOD17A2HGF"},{"href":"https://doi.org/10.5067/MODIS/MYD17A2HGF.061","rel":"cite-as","title":"LP DAAC - MYD17A2HGF"},{"href":"https://lpdaac.usgs.gov/data/data-citation-and-policies/","rel":"license","title":"LP DAAC - Data Citation and Policies"},{"href":"https://planetarycomputer.microsoft.com/dataset/modis-17A2HGF-061","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2HGF-061","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2HGF-061","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/collections/modis-17A2HGF-061/items/MYD17A2HGF.A2023361.h35v10.061.2024022201214","rel":"self","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=modis-17A2HGF-061&item=MYD17A2HGF.A2023361.h35v10.061.2024022201214","rel":"preview","title":"Map of item","type":"text/html"}],"children":[],"item":null,"assets":{"hdf":{"href":"https://modiseuwest.blob.core.windows.net/modis-061/MYD17A2HGF/35/10/2023361/MYD17A2HGF.A2023361.h35v10.061.2024022201214.hdf","type":"application/x-hdf","roles":["data"],"title":"Source data containing all bands"},"Gpp_500m":{"href":"https://modiseuwest.blob.core.windows.net/modis-061-cogs/MYD17A2HGF/35/10/2023361/MYD17A2HGF.A2023361.h35v10.061.2024022201214_Gpp_500m.tif","type":"image/tiff; 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application=geotiff; profile=cloud-optimized","roles":["data"],"title":"Quality control indicators","raster:bands":[{"data_type":"uint8","spatial_resolution":500}]},"tilejson":{"title":"TileJSON with default rendering","href":"https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=modis-17A2HGF-061&item=MYD17A2HGF.A2023361.h35v10.061.2024022201214&assets=Gpp_500m&tile_format=png&colormap_name=modis-17A2H%7CA2HGF&format=png","type":"application/json","roles":["tiles"]},"rendered_preview":{"title":"Rendered preview","rel":"preview","href":"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=modis-17A2HGF-061&item=MYD17A2HGF.A2023361.h35v10.061.2024022201214&assets=Gpp_500m&tile_format=png&colormap_name=modis-17A2H%7CA2HGF&format=png","roles":["overview"],"type":"image/png"}},"collection":"modis-17A2HGF-061","stac_extensions":["https://stac-extensions.github.io/projection/v1.1.0/schema.json","https://stac-extensions.github.io/raster/v1.1.0/schema.json"],"properties":{"created":"2024-01-22T20:12:14Z","updated":"2024-01-23T02:05:04.114000Z","datetime":null,"platform":"aqua","proj:epsg":null,"proj:wkt2":"PROJCS[\"unnamed\",GEOGCS[\"Unknown datum based upon the custom spheroid\",DATUM[\"Not specified (based on custom spheroid)\",SPHEROID[\"Custom spheroid\",6371007.181,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]","proj:shape":[2400,2400],"instruments":["modis"],"end_datetime":"2023-12-31T23:59:59Z","modis:tile-id":"51035010","proj:geometry":{"type":"Polygon","coordinates":[[[20015109.354,-2223901.039333],[20015109.354,-1111950.519667],[18903158.834333,-1111950.519667],[18903158.834333,-2223901.039333],[20015109.354,-2223901.039333]]]},"proj:transform":[463.3127165279165,0.0,18903158.834333,0.0,-463.3127165274999,-1111950.519667],"start_datetime":"2023-12-27T00:00:00Z","modis:vertical-tile":10,"modis:horizontal-tile":35},"bbox":[172.479315908761,-19.180676222654,180.143592885496,-9.97534112170732],"geometry":{"type":"Polygon","coordinates":[[[172.479315908761,-19.1662177463598],[180.143592885496,-19.180676222654],[179.999502554976,-9.97534112170732],[172.622770159185,-9.98364248250805],[172.479315908761,-19.1662177463598]]]},"id":"MYD17A2HGF.A2023361.h35v10.061.2024022201214","stac_version":"1.0.0"},"license":"proprietary","description":"The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. 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. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) 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.","sci:publications":[{"doi":"10.5067/MODIS/MOD17A2HGF.061","citation":"Running, S., & Zhao, M. (2021). MODIS/Terra Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A2HGF.061"},{"doi":"10.5067/MODIS/MYD17A2HGF.061","citation":"Running, S., & Zhao, M. (2021). MODIS/Aqua Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD17A2HGF.061"}],"assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A2HGF-061.png","type":"image/png","roles":["thumbnail"],"title":"MODIS Gross Primary Productivity 8-Day Gap-Filled thumbnail"},"geoparquet-items":{"href":"abfs://items/modis-17A2HGF-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"}}},"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 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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 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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. 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predictions.","raster:bands":[{"sampling":"area","data_type":"float32","spatial_resolution":30}]},"scmqa":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_SCMQA.tif","created":"2022-06-28T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220721_V13_CCDC.tif","created":"2022-07-21T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_LCACHG.tif","created":"2022-06-28T00:00:00Z","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 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Water"},{"name":"2_to_6","value":26,"color_hint":"ab00d6","description":"Cover change from Cropland to Wetlands"},{"name":"2_to_7","value":27,"color_hint":"ab00d6","description":"Cover change from Cropland to Snow/Ice"},{"name":"2_to_8","value":28,"color_hint":"ab00d6","description":"Cover change from Cropland to Barren"},{"name":"3_to_1","value":31,"color_hint":"ab00d6","description":"Cover change from Grass/Shrub to 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 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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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_SCLAST.tif","created":"2022-06-28T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_SCSTAB.tif","created":"2022-06-28T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_SCTIME.tif","created":"2022-06-28T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_LCPCONF.tif","created":"2022-06-28T00:00:00Z","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":{"href":"https://landcoverdata.blob.core.windows.net/lcmap/CU/V13/032003/2021/LCMAP_CU_032003_2021_20220721_V13_CCDC/LCMAP_CU_032003_2021_20220628_V13_LCSCONF.tif","created":"2022-06-28T00:00:00Z","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. 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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 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"}},"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"],"msft:short_description":"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3 Science Products for the Conterminous United States.","msft:region":"westeurope","title":"USGS LCMAP CONUS Collection 1.3","id":"usgs-lcmap-conus-v13","stac_version":"1.0.0","sci:publications":[{"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"},{"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"}],"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","extent":{"spatial":{"bbox":[[-129.27732,21.805095,-63.11843,52.92172]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2021-12-31T00:00:00Z"]]}},"msft:storage_account":"landcoverdata","msft:container":"lcmap","sci:doi":"10.5066/P9C46NG0","sci:citation":"U.S. Geological Survey (USGS), 2022, Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3 Science Products for the Conterminous United States: USGS data release","msft:group_id":"usgs-lcmap","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"]}]},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/cil-gdpcir-cc-by","rel":"self","type":"application/json"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html","title":"Creative Commons Attribution 4.0 International"},{"href":"https://zenodo.org/record/6403794","rel":"cite-as","type":"text/html"},{"href":"https://github.com/ClimateImpactLab/downscaleCMIP6/","rel":"describedby","title":"Project homepage","type":"text/html"},{"href":"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by","rel":"describedby","title":"Human readable dataset overview and 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Pa)\natmosChem: none\nland: JSBACH v3.1\nlandIce: none\nocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m)\nocnBgchem: none\nseaIce: CICE4.1","end_datetime":"2100-12-31T12:00:00Z","cmip6:license":"https://github.com/ClimateImpactLab/downscaleCMIP6/tree/master/data_licenses/NESM3.txt","cmip6:mip_era":"CMIP6","cmip6:product":"model-output","cmip6:table_id":"day","cube:variables":{"pr":{"type":"data","unit":"mm day-1","attrs":{"units":"mm day-1"},"shape":[31390,720,1440],"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')","history":"2019-08-11T09:50:24Z altered by CMOR: Treated scalar dimension: 'height'. 2019-08-11T09:50:24Z altered by CMOR: Inverted axis: lat.","long_name":"Daily Maximum Near-Surface Air Temperature","cell_methods":"area: mean time: 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China","cmip6:source_type":"AOGCM","cmip6:experiment_id":"ssp585","cmip6:forcing_index":1,"cmip6:physics_index":1,"cmip6:variant_label":"r1i1p1f1","cmip6:institution_id":"NUIST","cmip6:sub_experiment":"none","cmip6:further_info_url":"https://furtherinfo.es-doc.org/CMIP6.NUIST.NESM3.ssp585.none.r1i1p1f1","cmip6:realization_index":1,"cmip6:sub_experiment_id":"none","cmip6:data_specs_version":"01.00.30","cmip6:nominal_resolution":"250 km","cmip6:initialization_index":1},"stac_extensions":["https://stac-extensions.github.io/datacube/v2.0.0/schema.json"],"bbox":[-180,-90,180,90],"assets":{"pr":{"href":"abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr","cmip6:grid":"T63","msft:https-url":"https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr","cmip6:grid_label":"gn","cmip6:tracking_id":"hdl:21.14100/ed432434-922e-4cea-8400-c321598fd7cf\nhdl:21.14100/abea2bb8-09d0-4114-9051-fe232efe108e\nhdl:21.14100/205a607f-75f4-4919-b495-45fa8fdb77b8","cmip6:variable_id":"pr","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"rhgeuwest"}},"cmip6:creation_date":"2019-08-11T09:53:50Z","type":"application/vnd+zarr"},"tasmax":{"href":"abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr","cmip6:grid":"T63","msft:https-url":"https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr","cmip6:grid_label":"gn","cmip6:tracking_id":"hdl:21.14100/01f58d9c-1317-467e-813d-a2358cdf7954\nhdl:21.14100/e027cda7-ac28-4e00-b03f-53ad2e6d30b4\nhdl:21.14100/3898bc79-5174-4fcc-9637-4573914ad548","cmip6:variable_id":"tasmax","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"rhgeuwest"}},"cmip6:creation_date":"2019-08-11T09:50:24Z","type":"application/vnd+zarr"},"tasmin":{"href":"abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr","cmip6:grid":"T63","msft:https-url":"https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr","cmip6:grid_label":"gn","cmip6:tracking_id":"hdl:21.14100/1998e7f0-ad6a-4720-8150-9085db6f96b8\nhdl:21.14100/ca181871-4103-481d-8764-b74c2448427f\nhdl:21.14100/fceb22c4-0454-4d68-bb48-1d903df19e63","cmip6:variable_id":"tasmin","xarray:open_kwargs":{"chunks":{},"engine":"zarr","consolidated":true,"storage_options":{"account_name":"rhgeuwest"}},"cmip6:creation_date":"2019-08-11T09:48:45Z","type":"application/vnd+zarr"}},"id":"cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day"},"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"}},"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"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"msft:storage_account":"rhgeuwest","license":"CC-BY-4.0","stac_version":"1.0.0","msft:region":"westeurope","sci:doi":"10.5194/egusphere-2022-1513","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:group_id":"cil-gdpcir","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)","msft:short_description":"Climate Impact Lab Global Downscaled Projections for Climate Impacts Research 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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. 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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 STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.","msft:storage_account":"planet","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-analytic-thumbnail.png","type":"image/png","roles":["thumbnail"],"title":"thumbnail"},"geoparquet-items":{"href":"abfs://items/planet-nicfi-analytic.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"}}}},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/gnatsgo-rasters","rel":"self","type":"application/json"},{"href":"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf","rel":"about","description":"User guide for gSSURGO","type":"application/pdf","title":"gSSURGO User Guide"},{"href":"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1464658&ext=pdf","rel":"about","type":"application/pdf","description":"Slides giving a high level overview of the gNATSGO dataset","title":"gNATSGO Overview Slides"},{"href":"https://creativecommons.org/publicdomain/zero/1.0/","rel":"license","title":"CC0 1.0 Universal Public Domain Dedication"},{"href":"https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters","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/collections/gnatsgo-rasters/items/pw_-2391035_-539485_-2227195_-703325","rel":"self","type":"application/geo+json"},{"href":"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf","rel":"handbook","description":"Also includes data usage information","type":"application/pdf","title":"gSSURGO User Guide"},{"href":"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=gnatsgo-rasters&item=pw_-2391035_-539485_-2227195_-703325","rel":"preview","title":"Map of item","type":"text/html"}],"children":[],"item":null,"geometry":{"type":"Polygon","coordinates":[[[132.5857022,2.9267561],[132.5857022,4.5066595],[131.0295714,4.5066595],[131.0295714,2.9267561],[132.5857022,2.9267561]]]},"collection":"gnatsgo-rasters","bbox":[131.02957139,2.92675611,132.58570215,4.50665947],"assets":{"mukey":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/mukey_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1505147,"raster:bands":[{"nodata":2147483647.0,"data_type":"int32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-5_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519677,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-5_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509736,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-5a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1505610,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-5s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1508190,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-20_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519654,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-30_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519581,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws5-20_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1516813,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-20_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509802,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-30_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509790,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc5-20_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509803,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-20a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1505592,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-20s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1508172,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-30a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509924,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-30s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1508186,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk5-20a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1505607,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk5-20s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1508181,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-100_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519365,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-150_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519365,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws0-999_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1519404,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws20-50_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1516785,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/droughty_pw_-2391035_-539485_-2227195_-703325.tif","file:size":772642,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/nccpi3sg_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1510106,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-100_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509813,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-150_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509801,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc0-999_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509872,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc20-50_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509808,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-100a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515519,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-100s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509006,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-150a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515737,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-150s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509119,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-999a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515902,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk0-999s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509051,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk20-50a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515251,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk20-50s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1508634,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws50-100_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1516620,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/musumcpct_pw_-2391035_-539485_-2227195_-703325.tif","file:size":771635,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/nccpi3all_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1510161,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/nccpi3cot_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1510112,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/nccpi3soy_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1510080,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/pwsl1pomu_pw_-2391035_-539485_-2227195_-703325.tif","file:size":774262,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/rootznaws_pw_-2391035_-539485_-2227195_-703325.tif","file:size":773128,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/rootznemc_pw_-2391035_-539485_-2227195_-703325.tif","file:size":774003,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc50-100_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509820,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk50-100a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515507,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk50-100s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509011,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws100-150_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1516500,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/aws150-999_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1516172,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/musumcpcta_pw_-2391035_-539485_-2227195_-703325.tif","file:size":771657,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/musumcpcts_pw_-2391035_-539485_-2227195_-703325.tif","file:size":771653,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/nccpi3corn_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1510101,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/pctearthmc_pw_-2391035_-539485_-2227195_-703325.tif","file:size":773475,"raster:bands":[{"nodata":-9999.0,"data_type":"int16","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc100-150_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509817,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/soc150-999_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509859,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk100-150a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515687,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk100-150s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509010,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk150-999a_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1515725,"raster:bands":[{"nodata":2147483647.0,"data_type":"float32","spatial_resolution":10}],"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":{"href":"https://soils.blob.core.windows.net/gnatsgo/july2020/tiles/pw_-2391035_-539485_-2227195_-703325/tk150-999s_pw_-2391035_-539485_-2227195_-703325.tif","file:size":1509048,"raster:bands":[{"nodata":-9999.0,"data_type":"float32","spatial_resolution":10}],"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."},"tilejson":{"title":"TileJSON with default rendering","href":"https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=gnatsgo-rasters&item=pw_-2391035_-539485_-2227195_-703325&assets=aws0_100&colormap_name=cividis&rescale=0%2C600&format=png","type":"application/json","roles":["tiles"]},"rendered_preview":{"title":"Rendered preview","rel":"preview","href":"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=gnatsgo-rasters&item=pw_-2391035_-539485_-2227195_-703325&assets=aws0_100&colormap_name=cividis&rescale=0%2C600&format=png","roles":["overview"],"type":"image/png"}},"properties":{"datetime":"2020-07-01T00:00:00Z","proj:bbox":[-2391035.0,-703325.0,-2227195.0,-539485.0],"proj:epsg":null,"proj:wkt2":"PROJCS[\"Western_Pacific_Albers_Equal_Area_Conic\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",10],PARAMETER[\"longitude_of_center\",152.5],PARAMETER[\"standard_parallel_1\",8],PARAMETER[\"standard_parallel_2\",12],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]","proj:shape":[16384,16384],"proj:geometry":{"type":"Polygon","coordinates":[[[-2227195.0,-703325.0],[-2227195.0,-539485.0],[-2391035.0,-539485.0],[-2391035.0,-703325.0],[-2227195.0,-703325.0]]]},"proj:transform":[10.0,0.0,-2391035.0,0.0,-10.0,-539485.0]},"stac_extensions":["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/file/v2.0.0/schema.json"],"id":"pw_-2391035_-539485_-2227195_-703325","stac_version":"1.0.0"},"keywords":["Soils","NATSGO","SSURGO","STATSGO2","RSS","USDA","United States"],"msft:region":"westeurope","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.","extent":{"spatial":{"bbox":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},"temporal":{"interval":[["2020-07-01T00:00:00Z","2020-07-01T00:00:00Z"]]}},"stac_version":"1.0.0","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."}},"title":"gNATSGO Soil Database - Rasters","description":"This collection 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)","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 format.","msft:partition_info":{"is_partitioned":false},"table:storage_options":{"account_name":"pcstacitems"}}},"msft:group_id":"gnatsgo","providers":[{"url":"https://www.nrcs.usda.gov/","name":"United States Department of Agriculture, Natural Resources Conservation Service","roles":["licensor","producer","processor","host"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["processor","host"]}],"msft:container":"gnatsgo-stac","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"license":"CC0-1.0","id":"gnatsgo-rasters","msft:storage_account":"soils"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/sentinel-1-grd","rel":"self","type":"application/json"},{"href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","rel":"license","title":"Copernicus Sentinel data terms"},{"href":"https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd","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/collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20241028T045903_20241028T045913_056298_06E50B","rel":"self","type":"application/geo+json"},{"href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice","rel":"license"},{"href":"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=sentinel-1-grd&item=S1A_IW_GRDH_1SDV_20241028T045903_20241028T045913_056298_06E50B","rel":"preview","title":"Map of item","type":"text/html"}],"children":[],"item":null,"assets":{"vh":{"href":"https://sentinel1euwest.blob.core.windows.net/s1-grd/GRD/2024/10/28/IW/DV/S1A_IW_GRDH_1SDV_20241028T045903_20241028T045913_056298_06E50B_7194/measurement/iw-vh.tiff","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":{"href":"https://sentinel1euwest.blob.core.windows.net/s1-grd/GRD/2024/10/28/IW/DV/S1A_IW_GRDH_1SDV_20241028T045903_20241028T045913_056298_06E50B_7194/measurement/iw-vv.tiff","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":{"href":"https://sentinel1euwest.blob.core.windows.net/s1-grd/GRD/2024/10/28/IW/DV/S1A_IW_GRDH_1SDV_20241028T045903_20241028T045913_056298_06E50B_7194/preview/quick-look.png","type":"image/png","roles":["thumbnail"],"title":"Preview Image","description":"An averaged, decimated preview image in PNG format. 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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. 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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. 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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 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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 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"}},"msft:region":"westeurope","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":2,"minimum":0},"usgs_lcmap:horizontal_tile":{"maximum":4,"minimum":0}},"id":"usgs-lcmap-hawaii-v10","title":"USGS LCMAP Hawaii Collection 1.0","stac_version":"1.0.0","sci:doi":"10.5066/P91E8M23","msft:short_description":"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products for Hawaii","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"],"msft:group_id":"usgs-lcmap","msft:container":"lcmap","license":"proprietary","sci:publications":[{"doi":"10.1016/j.jag.2022.103015","citation":"Li, C., Xian, G., Wellington, D., Smith, K., Horton, J., & 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"}],"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. 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It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n","stac_version":"1.0.0","license":"proprietary","msft:short_description":"United States population counts at various levels of cartographic aggregation","msft:storage_account":"ai4edataeuwest","title":"US Census","keywords":["US Census Bureau","Administrative boundaries","Population","Demographics"],"msft:region":"westeurope","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"],"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"]]}},"table:tables":[{"name":"American Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)","description":"This file contains data for legal and statistical [American Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1) entities published by the US Census Bureau.","msft:item_name":"cb_2020_us_aiannh_500k"},{"name":"American Indian Tribal Subdivisions (AITSN)","description":"This file contains data on [American Indian Tribal Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1). These areas are the legally defined subdivisions of American Indian Reservations (AIR), Oklahoma Tribal Statistical Areas (OTSA), and Off-Reservation Trust Land (ORTL).","msft:item_name":"cb_2020_us_aitsn_500k"},{"name":"Alaska Native Regional Corporations (ANRC)","description":"This file contains data on [Alaska Native Regional Corporations](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1), which are corporations created according to the Alaska Native Claims Settlement Act. ","msft:item_name":"cb_2020_02_anrc_500k"},{"name":"Tribal Block Groups (TBG)","description":"This file includes data on [Tribal Block Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_26), which are subdivisions of Tribal Census Tracts. 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","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Dataset 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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 (CH4) 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 (NO2) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O3) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O3) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO2) 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","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"],"title":"Sentinel-5P Level-2","keywords":["ESA","Copernicus","Sentinel","Air Quality","Climate 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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"}],"start_datetime":"2023-02-26T00:00:00Z"},"assets":{"data":{"href":"abfs://eclipse/Chicago/2023-02-26/20230226-00_20230305-00.parquet","type":"application/x-parquet","roles":["data"],"title":"Weekly dataset","table:storage_options":{"account_name":"ai4edataeuwest"}}},"stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"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"}],"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"]}],"msft:region":"westeurope","extent":{"spatial":{"bbox":[[-87.94011408252348,41.64454312178303,-87.5241371038952,42.023038586147585]]},"temporal":{"interval":[["2021-01-01T00:00:00Z",null]]}},"title":"Urban Innovation Eclipse Sensor Data","stac_version":"1.0.0","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","msft:container":"eclipse","license":"proprietary","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Weekly dataset","table:storage_options":{"account_name":"ai4edataeuwest"}}},"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","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","id":"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"],"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"}}},"keywords":["Eclipse","PM25","air pollution"]},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-palsar-mosaic/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/alos-palsar-mosaic","rel":"self","type":"application/json"},{"href":"https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_Mosaic_V200.pdf","rel":"handbook","type":"application/pdf","title":"Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)","description":"Also includes data usage information"},{"href":"https://earth.jaxa.jp/policy/en.html","rel":"license","title":"JAXA Terms of Use of Research Data","type":"text/html"},{"href":"https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic","rel":"describedby","type":"text/html","title":"Human readable dataset overview and reference"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-palsar-mosaic","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-palsar-mosaic","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/collections/alos-palsar-mosaic/items/S56W072_21_F02DAR_MOS","rel":"self","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=alos-palsar-mosaic&item=S56W072_21_F02DAR_MOS","rel":"preview","type":"text/html","title":"Map of item"}],"children":[],"item":null,"geometry":{"type":"Polygon","coordinates":[[[-71,-57],[-71,-56],[-72,-56],[-72,-57],[-71,-57]]]},"bbox":[-72.0,-57.0,-71.0,-56.0],"properties":{"cf":"83.0 dB","gsd":25,"title":"S56W072_21_F02DAR_MOS","datetime":"2021-01-01T00:00:00Z","platform":"ALOS-2","proj:bbox":[-72.0,-57.0,-71.0,-56.0],"proj:epsg":4326,"proj:shape":[4500,4500],"description":"Annual PALSAR Mosaic","instruments":["PALSAR-2"],"end_datetime":"2021-12-31T23:59:59Z","proj:transform":[0.00022222222222222223,0.0,-72.0,0.0,-0.00022222222222222223,-56.0,0.0,0.0,1.0],"start_datetime":"2021-01-01T00:00:00Z","sat:orbit_state":"ascending","sar:product_type":"GTC","sar:polarizations":["HH","HV"],"palsar:beam_number":"02","sar:frequency_band":"L","sar:instrument_mode":"F","sar:observation_direction":"right","palsar:number_of_polarizations":"D"},"assets":{"HH":{"href":"https://pceo.blob.core.windows.net/palsar/v200/alos_palsar_mosaic/2021/S55W075/S56W072_21_sl_HH_F02DAR.tif","roles":["data"],"raster:bands":[{"nodata":1,"data_type":"uint16"}],"type":"image/tiff; 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application=geotiff; profile=cloud-optimized","title":"linci","description":"Local incidence angle (degrees)."},"metadata":{"type":"application/xml","title":"metadata","description":"Product metadata file"}},"msft:region":"westeurope","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/sat/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"],"title":"ALOS PALSAR Annual Mosaic","extent":{"spatial":{"bbox":[[-180.0,85.0,180.0,-56.0]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"proprietary","keywords":["ALOS","JAXA","Remote Sensing","Global"],"description":"Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)","stac_version":"1.0.0","msft:container":"palsar"},{"type":"Collection","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings/items","rel":"items","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","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/collections/ms-buildings","rel":"self","type":"application/json"},{"href":"https://github.com/microsoft/GlobalMLBuildingFootprints/blob/main/LICENSE","rel":"license","type":"text/html","title":"ODbL-1.0 License"},{"href":"https://planetarycomputer.microsoft.com/dataset/ms-buildings","rel":"describedby","title":"Human readable dataset overview and reference","type":"text/html"}],"children":[],"item":{"type":"Feature","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings","rel":"collection","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings","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/collections/ms-buildings/items/WesternHalaibTriangle_122310000_2023-04-25","rel":"self","type":"application/geo+json"}],"children":[],"item":null,"properties":{"title":"Building footprints","datetime":"2023-04-25T00:00:00Z","description":"Parquet dataset with the building footprints","table:columns":[{"name":"geometry","type":"byte_array","description":"Building footprint polygons"},{"name":"meanHeight","type":"float"}],"table:row_count":130,"msbuildings:region":"WesternHalaibTriangle","msbuildings:quadkey":122310000,"msbuildings:processing-date":"2023-04-25"},"stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json"],"stac_version":"1.0.0","bbox":[33.75310880749859,21.821240707701463,33.895686222188225,21.861457041503243],"collection":"ms-buildings","assets":{"data":{"href":"abfs://footprints/delta/2023-04-25/ml-buildings.parquet/RegionName=WesternHalaibTriangle/quadkey=122310000","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"}}},"geometry":{"type":"Polygon","coordinates":[[[34.453125,21.28937435586042],[34.453125,21.943045533438177],[33.75,21.943045533438177],[33.75,21.28937435586042],[34.453125,21.28937435586042]]]},"id":"WesternHalaibTriangle_122310000_2023-04-25"},"license":"ODbL-1.0","msft:storage_account":"bingmlbuildings","id":"ms-buildings","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. 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84","conformsTo":["https://api.stacspec.org/v1.0.0/core","https://api.stacspec.org/v1.0.0/collections","https://api.stacspec.org/v1.0.0/ogcapi-features","https://api.stacspec.org/v1.0.0/item-search","https://api.stacspec.org/v1.0.0/ogcapi-features#fields","https://api.stacspec.org/v1.0.0/ogcapi-features#sort","https://api.stacspec.org/v1.0.0/ogcapi-features#query","https://api.stacspec.org/v1.0.0/item-search#fields","https://api.stacspec.org/v1.0.0/item-search#sort","https://api.stacspec.org/v1.0.0/item-search#query","https://api.stacspec.org/v0.3.0/aggregation","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson"],"description":"A STAC API of public datasets on AWS","id":"earth-search-aws"}],"item":null} \ No newline at end of file diff --git a/src/config.rs b/src/config.rs new file mode 100644 index 0000000..b747ce4 --- /dev/null +++ b/src/config.rs @@ -0,0 +1,56 @@ +use anyhow::{Error, Result}; +use serde::Deserialize; +use stac::{Catalog, Link}; +use std::{ + collections::HashMap, + fs::File, + io::{BufReader, Read}, + path::Path, +}; + +#[derive(Debug, Deserialize)] +pub struct Config { + catalog: Catalog, + catalogs: HashMap, +} + +#[derive(Debug, Deserialize)] +struct CatalogConfig { + href: String, + title: String, + index: usize, +} + +impl Config { + pub fn from_path(path: impl AsRef) -> Result { + let mut file = BufReader::new(File::open(path)?); + let mut s = String::new(); + file.read_to_string(&mut s)?; + toml::from_str(&s).map_err(Error::from) + } + + pub async fn crawl(self) -> Result { + crate::crawl(self.into_catalog()?).await + } + + pub fn into_catalog(mut self) -> Result { + for (id, catalog_config) in &self.catalogs { + let mut link = + Link::child(&catalog_config.href).title(Some(catalog_config.title.clone())); + // Once https://github.com/stac-utils/stac-rs/issues/501 lands this should be cleaner + link.additional_fields + .insert("heystac:id".into(), id.as_str().into()); + link.additional_fields + .insert("heystac:index".into(), catalog_config.index.into()); + self.catalog.links.push(link); + } + self.catalog.links.sort_by_key(|c| { + c.additional_fields + .get("heystac:index") + .unwrap() + .as_i64() + .unwrap() + }); + Ok(self.catalog) + } +} diff --git a/src/crawl.rs b/src/crawl.rs new file mode 100644 index 0000000..f98acfe --- /dev/null +++ b/src/crawl.rs @@ -0,0 +1,118 @@ +use anyhow::{Error, Result}; +use reqwest::{Client, Url}; +use serde::{Deserialize, Serialize}; +use serde_json::Value; +use stac::Catalog; +use std::{collections::HashMap, future::Future, pin::Pin}; +use tokio::task::JoinSet; + +pub async fn crawl(catalog: Catalog) -> Result { + let client = Client::new(); + crawl_value(catalog.try_into()?, client).await?.try_into() +} + +fn crawl_value( + mut value: CrawlValue, + client: Client, +) -> Pin>>> { + Box::pin(async move { + let mut join_set: JoinSet> = JoinSet::new(); + match value.r#type.as_str() { + "Catalog" => { + for link in value.links.iter().filter(|link| link.rel == "child") { + let href = link.href.clone(); + let client = client.clone(); + tracing::info!("getting child: {href}"); + let _ = join_set.spawn(async move { + client + .get(href) + .send() + .await? + .error_for_status()? + .json() + .await + .map_err(Error::from) + }); + } + } + "Collection" => { + if let Some(link) = value.links.iter().find(|link| link.rel == "item") { + let url = Url::parse_with_params( + &link.href, + [("limit", "1"), ("sortby", "-properties.datetime")], + )?; + tracing::info!("getting item: {}", url); + value.item = client + .get(url) + .send() + .await? + .error_for_status()? + .json() + .await?; + } + if value.item.is_none() { + if let Some(link) = value.links.iter().find(|link| link.rel == "items") { + // TODO sort items, maybe limit? + tracing::info!("getting items: {}", link.href); + let mut items: CrawlValue = reqwest::get(&link.href) + .await? + .error_for_status()? + .json() + .await?; + if !items.features.is_empty() { + value.item = Some(items.features.remove(0)); + } + } + } + } + _ => {} + } + while let Some(result) = join_set.join_next().await { + let child = result??; + let client = client.clone(); + let child = crawl_value(child, client).await?; + value.children.push(Box::new(child)); + } + Ok(value) + }) +} + +// We use a very limited STAC value representation to parse as permissively as possible. +#[derive(Debug, Deserialize, Serialize)] +struct CrawlValue { + r#type: String, + #[serde(default)] + links: Vec, + #[serde(default)] + children: Vec>, + #[serde(default)] + item: Option>, + #[serde(default, skip_serializing_if = "Vec::is_empty")] + features: Vec>, + #[serde(flatten)] + additional_fields: HashMap, +} + +#[derive(Debug, Deserialize, Serialize)] +struct CrawlLink { + href: String, + rel: String, + #[serde(flatten)] + additional_fields: HashMap, +} + +impl TryFrom for CrawlValue { + type Error = Error; + + fn try_from(value: Catalog) -> Result { + serde_json::from_value(serde_json::to_value(value)?).map_err(Error::from) + } +} + +impl TryFrom for Catalog { + type Error = Error; + + fn try_from(value: CrawlValue) -> Result { + serde_json::from_value(serde_json::to_value(value)?).map_err(Error::from) + } +} diff --git a/src/lib.rs b/src/lib.rs index f484d06..d98ed01 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,54 +1,4 @@ -use anyhow::{Error, Result}; -use serde::Deserialize; -use stac::{Catalog, Link}; -use std::{ - collections::HashMap, - fs::File, - io::{BufReader, Read}, - path::Path, -}; +mod config; +mod crawl; -#[derive(Debug, Deserialize)] -pub struct Config { - catalog: Catalog, - catalogs: HashMap, -} - -#[derive(Debug, Deserialize)] -struct CatalogConfig { - href: String, - title: String, - index: usize, -} - -impl Config { - pub fn from_path(path: impl AsRef) -> Result { - let mut file = BufReader::new(File::open(path)?); - let mut s = String::new(); - file.read_to_string(&mut s)?; - toml::from_str(&s).map_err(Error::from) - } - - pub fn write_catalog(&self, path: impl AsRef) -> Result<()> { - let mut catalog = self.catalog.clone(); - for (id, catalog_config) in &self.catalogs { - let mut link = - Link::child(&catalog_config.href).title(Some(catalog_config.title.clone())); - // Once https://github.com/stac-utils/stac-rs/issues/501 lands this should be cleaner - link.additional_fields - .insert("heystac:id".into(), id.as_str().into()); - link.additional_fields - .insert("heystac:index".into(), catalog_config.index.into()); - catalog.links.push(link); - } - catalog.links.sort_by_key(|c| { - c.additional_fields - .get("heystac:index") - .unwrap() - .as_i64() - .unwrap() - }); - let file = File::create(path)?; - serde_json::to_writer_pretty(file, &catalog).map_err(Error::from) - } -} +pub use {config::Config, crawl::crawl}; diff --git a/src/main.rs b/src/main.rs index cd6f43c..236755c 100644 --- a/src/main.rs +++ b/src/main.rs @@ -9,18 +9,34 @@ struct Args { #[derive(Debug, clap::Subcommand)] enum Subcommand { + /// Crawl all catalogs + Crawl, + /// Run the prebuild actions Prebuild, } -fn main() { +#[tokio::main] +async fn main() { + tracing_subscriber::fmt::init(); let args = Args::parse(); + let config = Config::from_path("config.toml").unwrap(); match args.subcommand { - Subcommand::Prebuild => prebuild(), + Subcommand::Crawl => { + let catalogs = config.crawl().await.unwrap(); + std::fs::write( + "crawl/crawl.json", + serde_json::to_string(&catalogs).unwrap(), + ) + .unwrap(); + } + Subcommand::Prebuild => { + let catalog = config.into_catalog().unwrap(); + std::fs::write( + "app/catalog.json", + serde_json::to_string_pretty(&catalog).unwrap(), + ) + .unwrap(); + } } } - -fn prebuild() { - let config = Config::from_path("config.toml").unwrap(); - config.write_catalog("app/catalog.json").unwrap(); -}