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NOAA US Climate Reference Network



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  • Tutorial on Creating a Google Fusion Table from a KML File

    ClimateEngineTutorial_ImportCustomRegions from Climate Engine on Vimeo.

    Note: the images in this tutorial do not currently match Climate Engine, but are very close. Additionally, fusion tables will stop working in Climate Engine on August 31, 2019, where we will transition completely over to utilizing table assets from shape files instead.

    Tutorial on Creating a Table Asset from a Shape File

    Step 1: Get a Google Earth Engine account - apply here.

    Step 2: Log into the Google Code Playground - go here.

    Import a shape file to the Asset Manager - follow these directions from GEE. Note that Climate Engine can only load the most simple of shape files, where there is only 1 level of names for the geography boundaries. Multiple levels in the structure of the shape file will not work in Climate Engine.

    Step 4: Make your table asset public by setting sharing permissions - follow these directions.

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    Troubleshooting: If your table asset will not load in Climate Engine, either the path is not correct or the table asset is too large and/or complicated. In the latter case, either reduce the size of the asset or simplify the geometries in the asset.

    If you need some help to get this working, email us at climateengine@gmail.com.

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    Huntington, J., Hegewisch, K., Daudert, B., Morton, C., Abatzoglou, J., McEvoy, D., and T., Erickson. (2017). Climate Engine: Cloud Computing of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding. Bulletin of the American Meteorological Society, http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00324.1

    Cloud Computing of Climate and Remote Sensing Data





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    Data

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    • ACIS NRCC NN
    • ANUSPLIN
    • CEMS FIRE
    • CFS
    • CFS GRIDMET
    • CFS GRIDMET DAILY
    • CHIRPS
    • DAYMET
    • CPC CMORPH
    • CPC UPP
    • Earthquake
    • ERA5
    • ERA5 Ag
    • ERA5 Land Daily
    • ESI
    • FLDAS
    • FRET
    • GEPS
    • GLDAS
    • GPM
    • gridMET
    • gridMET Drought
    • HRDPA
    • HRDPS
    • Landsat
    • MERRA2
    • MERRA2 FWI
    • MODIS
    • MODIS ET MOD16
    • MODIS ET PML V2
    • MODIS ET SSEBOP
    • MODIS Fire Burned Area
    • MTBS
    • NADM
    • NCEP NCAR
    • NCLIM
    • NLDAS2
    • NOAA OISST
    • OPENET
    • PERSIANN CDR
    • PRISM
    • RAP
    • RDPA
    • RDPS
    • RTMA
    • Sentinel 2
    • Sentinel 5P
    • SNODAS
    • TerraClimate
    • TRMM
    • USDM
    • US DROUGHT
    • CAN Drought
    • WRC

    ACIS

    • Description: The ACIS Climate Maps are produced daily using data from the Applied Climate Information System (ACIS). Station data in ACIS primarily come from the following networks:
      • National Weather Service Cooperative Observer Program (NWS COOP)
      • Weather-Bureau-Army-Navy/Automated Surface Observing System (WBAN/ASOS)
      • Snow Telemetry (SNOTEL)
      • Community Collaborative Rain, Hail, & Snow (CoCoRaHS) Network
      • Remote Automatic Weather Stations (RAWS)
      All near-real-time data are considered preliminary and subject to change.
    • Organization: Applied Climate Information System (ACIS)
    • Spatial resolution: 5-km (0.04-deg x 0.04-deg)
    • Time Span: 1951-01-01 to Present (updated every 1-2 weeks)
    • Variables:
      • Minimum/maximum daily temperature
      • Daily precipitation
    • Website: ACIS Website
    • Google Earth Engine Catalog: ACIS is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.

    ANUSPLIN Gridded Climate Dataset for Canada (DAILY, MONTHLY)

    • Description: The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily timesteps for maximum temperature, minimum temperature, and total precipitation at 10km resolution.
    • Organization: Agriculture and Agri-food Canada
    • Spatial resolution: 10-km (~0.1-deg)
    • Time Span: 1950-01-01 to 2015-12-31 (Daily and Monthly)
    • Variables:
      • Minimum/Maximum Temperature (mint/maxt)
      • Precipitation (pcp)
    • Website:
      • Daily models
      • Historical monthly climate grids for Canada and the United States
    • Google Earth Engine Catalog: ANUSPLIN is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009). "Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003." American Meteorological Society(April): 725-741.
      • McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011). "Customized spatial climate models for North America." Bulletin of American Meteorological Society-BAMS December: 1612-1622.

    CEMS Fire Danger Indices

    • Description: Fire danger indices from the ECMWF, calculated using weather forecasts from historical simulations provided by ECMWF ERA5 reanalysis.
    • Organization: European Centre for Medium-Range Weather Forecasts(ECMWF)/Copernicus Emergency Management Service
    • Spatial resolution: ~0.8-km (1/100-deg)
    • Time Span: 2021-06-01 to Present (updated first week of each month)
    • Variables:
      • Fire Weather Index - a numeric rating of fire intensity. It is based on the spread component and the build up index, and is used as a general index of fire danger throughout the forested areas of Canada.
      • Fire Danger Risk - a relative index of how easy it is to ignite vegetation, how difficult a fire may be to control, and how much damage a fire may do.
      • Fire Daily Severity Rating - a numeric rating of the difficulty of controlling fires. It is based on the Fire Weather Index but it more accurately reflects the expected effort required for fire suppression.
      • Burning Index - a number related to the contribution of fire behavior to the effort of containing a fire. The BI is derived from a combination of Spread and Energy Release Components. It is expressed as a numeric value closely related to the flame length in feet multiplied by ten. It is a component of the NFDRS-National Fire Danger Rating System.
      • Energy Release Component - a number related to the available energy (BTU) per unit area (square foot) within the flaming front at the head of a fire. The ERC is considered a composite fuel moisture index as it reflects the contribution of all live and dead fuels to potential fire intensity. It is a component of the NFDRS-National Fire Danger Rating System.
      • Ignition Component- a number which relates the probability that a fire will result if a firebrand is introduced into a fine fuel complex. It is a component of the NFDRS-National Fire Danger Rating System.
      • Spread Component - a rating of the forward rate of spread of a head fire. It integrates the effect of wind, slope, and fuel bed and fuel particle properties. The daily variations are caused by the changes in the wind and moisture contents of the live fuels and the dead fuel timelag classes of 1, 10, and 100 hr. It is a component of the NFDRS-National Fire Danger Rating System.
    • Website: CEMS Website
    • Google Earth Engine Catalog: This raster dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11. 'Contains modified Copernicus Climate Change Service information [Year]'

    DAYMET

    • Datasets:
      • Daymet V4: Daily Surface Weather and Climatological Summaries
    • Description: Daymet V4 provides gridded estimates of daily weather parameters for Continental North America, Hawaii, and Puerto Rico (Data for Puerto Rico is available starting in 1950). It is derived from selected meteorological station data and various supporting data sources.
    • Organization: NASA ORNL DACC at Oak Ridge National Library
    • Website: DAYMET Website
    • Google Earth Engine Catalog:
      • DAYMET Info (NASA/ORNL/DAYMET_V4)
    • Spatial resolution: 1-km grid (1/96-deg)
    • Time Span: 1980 to present full year (updates processed at the close of a calendar year)
    • Variables:
      • Minimum/Maximum/Mean Temperature
      • Precipitation
      • Vapor Pressure
      • Downward Shortwave Radiation - Climate Engine is providing a 24-hour by modifying the daylight-average provided by Daymet.
      • Snow Water Equivalent
      • Standardized precipitation index (SPI)
      • Potential Evapotranspiration (Hargreaves method)
      • Potential Water Deficit (precipitation minus potential evapotranspiration)
      • Evaporative Drought Demand Index (EDDI) utilizing the Hargreaves PET
    • References:
      • Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840

    gridMET

    • Datasets:
      • gridMET (aka METDATA)
    • Description: Surface meteorological dataset based on PRISM and NLDAS-2
    • Organization: University of California Merced (UCM)
    • Website: gridMET Website
    • Google Earth Engine Catalog: gridMET Info (IDAHO_EPSCOR/GRIDMET)
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: 1979 to Present (updated every day)
    • Variables:
      • Minimum/Maximum Temperature
      • Precipitation
      • Minimum/Maximum Relative Humidity
      • Specific Humidity
      • Downward Solar Radiation
      • Near Surface Wind Velocity
      • ASCE Grass/Alfalfa Reference Evapotranspiration
      • Energy Release Component(model-G)
      • Burning Index (model-G)
      • 100-hr & 1000-hr Fuel Moisture (model-G)
    • References:
      • Abatzoglou J. T. "Development of gridded surface meteorological data for ecological applications and modelling". International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract)

    gridMET Drought Indices

    • Datasets:
      • gridMET Drought Indices
    • Description: The gridMET drought indices are computed using the daily gridMET dataset (which is a surface meteorological dataset based on PRISM and NLDAS-2). The drought indices computed are for the standardized indices of SPI, SPEI and EDDI and the Palmer Drought Severity Index (PDSI) and Palmer Z-Index. The methods used to compute these indices from gridMET are described here.
    • Organization: University of California Merced (UCM)
    • Website: gridMET Website
    • Google Earth Engine Catalog:
      • METDATA DROUGHT Info (GRIDMET DROUGHT)
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: 1979 to Present (updated every 5 days with a 4-9 day lag)
    • Variables:
      • Palmer Drought Severity Index (PDSI)
      • Palmer Z-Index (Z)
      • Standardized Precipitation Index (SPI) (14,30,60,90,180-day, 1,2,5-year)
      • Standardized Precipitation Evapotranspiration Index (SPEI) (14,30,60,90,180-day, 1,2,5-year)
      • Evaporative Demand Drought Index (EDDI) (14,30,60,90,180-day, 1,2,5-year)
      • Short and Long Term Drought Blends (Computed by Climate Engine)
    • References:
      • Abatzoglou J. T. "Development of gridded surface meteorological data for ecological applications and modelling". International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract)

    PRISM

    • Datasets:
      • PRISM (daily and monthly aggregations)
    • Description: Gridded climate datasets for the conterminous United States
    • Organization: Oregon State University
    • Website: PRISM Website
    • Google Earth Engine Catalog:
      • PRISM DAILY Info (OREGONSTATE/PRISM/AN81d)
      • PRISM MONTHLY Info (OREGONSTATE/PRISM/AN81m)
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: 1981 to Present (updated every day)
    • Variables:
      • Minimum/Maximum/Mean Temperature
      • Mean Dew Point Temperature
      • Precipitation
      • Minimum/Maximum Vapor Pressure Deficit
      • Potential Evapotranspiration (Hargreaves method)
      • Potential Water Deficit (precipitation minus potential evapotranspiration)
      • Standardized precipitation index (SPI)
      • Evaporative Drought Demand Index (EDDI) utilizing the Hargreaves PET
      • Standardized precipitation evapotranspiration index (SPEI)
    • References:
      • Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.(PDF)
      • Daly, C., J.I. Smith, and K.V. Olson. 2015. Mapping atmospheric moisture climatologies across the conterminous United States. PloS ONE 10(10):e0141140. doi:10.1371/journal.pone.0141140. (PDF)

    HRDPA

    • Datasets:
      • HRDPA Dataset
    • Description: The High Resolution Deterministic Precipitation Analysis(HRDPA) is a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. CaPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. HRDPA is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies. The model is based on the Canadian Precipitation Analysis (CaPA) system.
    • Organization: Meterological Service of Canada (MSC)
    • Website:
    • Google Earth Engine Catalog: HRDPA is not publicly available on Google Earth Engine yet but can be accessed here. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 2.5 km grid (1/24 deg)
    • Time Span: 2018 to Present (updated every day)
    • Variables:
      • Precipitation
    • References:
      • Canadian Precipitation Analysis (CaPA) Methodology system

    HRDPS

    • Datasets:
      • HRDPS Dataset
    • Description: The High Resolution Deterministic Prediction System(HRDPS) provides useful numerical simulations of temperature over large areas. Climate Engine is ingesting only the band containing temperature at 2m above ground level, but HRDPS also produces bands for precipiation, cloud cover, wind speed and direction, humidity, and others. These numerical simulations can be used for air quality modeling and forecasting, climate and wildfire modeling, and extreme weather forecasting. Users who will benefit most from using these new data are those for whom a detailed forecast of surface temperatures and winds is important. The 2.5 km forecasts could add much value especially during the change of seasons and in wintertime when rapid changes in temperature and winds cause phase transitions of precipitation (freezing rain to snow to rain for example). HRDPS is the high resolution counterpart to the RDPS dataset.
    • Organization: Environment and Climate Change Canada
    • Website: RDPS Data Website
    • Google Earth Engine Catalog: RDPS is not publicly available on Google Earth Engine yet but can be accessed here. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 2.5 km grid (1/24 deg)
    • Time Span: 2015 to Present (updated every day)
    • Variables:
      • 2-m Mean Temperature
    • References:
      • HRDPS Data website

    RDPA

    • Datasets:
      • RDPA Dataset
    • Description: The Regional Deterministic Precipitation Analysis (RDPA) based on the Canadian Precipitation Analysis (CaPA) system is on a domain that corresponds to that of the operational regional model, i.e. the Regional Deterministic Prediction System (RDPS-LAM3D) except for areas over the Pacific ocean where the western limit of the RDPA domain is slightly shifted eastward with respect to the regional model domain. The resolution of the RDPA analysis is identical to the resolution of the operational regional system RDPS LAM3D. The fields in the RDPA GRIB2 dataset are on a polar-stereographic (PS) grid covering North America and adjacent waters with a 10 km resolution at 60 degrees north.
    • Organization: Canadian Meteorological Centre
    • Website: RDPA Website
    • Google Earth Engine Catalog: RDPA is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 10.0 km grid (1/11 deg)
    • Time Span: 2003 to Present (updated every day)
    • Variables:
      • Precipitation
    • References:
      • Canadian Precipitation Analysis (CaPA) Methodology system

    RDPS

    • Datasets:
      • RDPS Dataset
    • Description: The Regional Deterministic Prediction System (RDPS) carries out physics calculations to arrive at deterministic predictions of atmospheric elements from the current day out to 48 hours into the future. The data for mean temperature covers North America and is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year. MSC also provides federal department, agencies and other levels of government with information to support emergency preparedness and response to events such as storms, floods, wildfires and other weather-related emergencies.
    • Organization: Environment and Climate Change Canada
    • Website: RDPS Data Website
    • Google Earth Engine Catalog: RDPS is not publicly available on Google Earth Engine yet but can be accessed here. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 10.0 km grid (1/11 deg)
    • Time Span: 2010 to Present (updated every day)
    • Variables:
      • 2-m Mean Temperature
    • References:
      • RDPS Data Website

    RTMA

    • Datasets:
      • Real-Time Mesoscale Analysis(RTMA) Dataset
    • Description: The Real-Time Mesoscale Analysis (RTMA) is a high-spatial and temporal resolution analysis for near-surface weather conditions. This dataset includes hourly analyses at 2.5 km for CONUS.
    • Organization: NOAA/NWS
    • Website: NCEP RTMA Website
    • Google Earth Engine Catalog: RTMA Info (NOAA/NWS/RTMA)
    • Spatial resolution: 2.5-km grid
    • Time Span: Jan 1, 2011 - Present
    • Variables:
      • Min, Max, Dew Point Temperature
      • ASCE Alfalfa Reference Evapotranspiration (ETr)
      • ASCE Grass Reference Evapotranspiration (ETo)
      • Specific Humidity
      • Incoming Shortwave Radiation
      • Total Cloud Cover
      • Air Pressure
      • Wind Speed
      • Wind Direction (from which blowing)
    • References:
      • De Pondeca, M. S. F. V., Manikin, G. S., DiMego, G., Benjamin, S. G., Parrish, D. F., Purser, R. J., Wu, W., Horel, J. D., Myrick, D. T., Lin, Y., Aune, R. M., Keyser, D., Colman, B., Mann, G., & Vavra, J. (2011). The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development, Weather and Forecasting, 26(5), 593-612. Retrieved Sep 19, 2022, from https://journals.ametsoc.org/view/journals/wefo/26/5/waf-d-10-05037_1.xml

    TerraClimate

    • Datasets:
      • TerraClimate
    • Description: Climate and climate water balance dataset for terrestrial surfaces based on WorldClim and CRU Ts4.0 and JRA55
    • Organization: University of California Merced (UCM)
    • Website: TerraClimate Website
    • Google Earth Engine Catalog: TerraClimate Info
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: 1958 to Present (monthly data, updated every year)
    • Variables:
      • Actual evapotranspiration
      • Climate water deficit
      • Palmer drought severity index (PDSI)
      • Reference evapotranspiration(ASCE grass/alfalfa)
      • Precipitation accumulation
      • Runoff
      • Soil moisture
      • Downward solar radiation
      • Snow water equivalent
      • Minimum/Maximum Temperature
      • Vapor Pressure
      • Vapor Pressure Deficit
      • Wind Speed (10-m)
    • References:
      • Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data 5:170191, doi: 10.1038/sdata.2017.191. (Abstract)

    Landsat 5/7/8/9 Remote Sensing

    • Datasets:
      • Landsat 5 Top of Atmosphere (TOA)
      • Landsat 7 Top of Atmosphere (TOA)
      • Landsat 8 Top of Atmosphere (TOA)
      • Landsat 9 Top of Atmosphere (TOA)
      • Landsat 5/7/8/9 Top of Atmosphere (TOA)
      • Landsat 5 Surface Reflectance (SR)
      • Landsat 7 Surface Reflectance (SR)
      • Landsat 8 Surface Reflectance (SR)
      • Landsat 9 Surface Reflectance (SR)
      • Landsat 5/7/8/9 Surface Reflectance (SR)
    • Description: Reflectance data in visible, near, and short wave infrared and thermal bands
    • Organization: NASA/USGS
    • Website: Landsat Website
    • Google Earth Engine Catalog: the following Google Earth Engine products are used in Climate Engine
      • Landsat 5 SR Info(LANDSAT/LT05/C02/T1_L2)
      • Landsat 7 SR Info(LANDSAT/LE07/C02/T1_L2)
      • Landsat 8 SR Info (LANDSAT/LC08/C02/T1_L2)
      • Landsat 9 SR Info (LANDSAT/LC09/C02/T1_L2)
      • Landsat 5 TOA Info (LANDSAT/LT05/C02/T1_TOA)
      • Landsat 7 TOA Info (LANDSAT/LE07/C02/T1_TOA)
      • Landsat 8 TOA Info (LANDSAT/LC08/C02/T1_TOA)
      • Landsat 9 TOA Info (LANDSAT/LC09/C02/T1_TOA)
      The following products are concatenations of the datasets above:
      • Landsat 5/7/8/9 Top of Atmosphere (TOA)
      • Landsat 5/7/8/9 Surface Reflectance (SR)
    • Spatial resolution:
      • Landsat 5: 30-m & 120-m (thermal)
      • Landsat 7: 30-m & 60-m (thermal)
      • Landsat 8: 30-m & 100-m (thermal)
      • Landsat 9: 30-m & 100-m (thermal)
    • Time Span: 1984 to Present (updated every 8/16-days)
    • Variables:
      • EVI: Enhanced Vegetation Index
      • LST: Land Surface Temperature
      • NDVI: Normalized Difference Vegetation Index
      • NDSI: Normalized Difference Snow Index
      • NDWI: Normalized Difference Water Index
    • Methods:
    • ClimateEngine applies a cloud mask to the Landsat TOA/SR data. The cloud masking attempts to remove medium and high confidence snow, shadow and cirrus clouds using the BQA quality band provided in the Landsat GEE collection.
      • Surface reflectance product guides: Landsat 4-7 SR, Landsat 8 SR
      • Landsat Handbooks: Landsat 7, Landsat 8
      • Landsat Guide to Level-1 Products

    MODIS Remote Sensing

    • Datasets:
      • MODIS Terra Daily
      • MODIS Terra 8-day
      • MODIS Terra 16-day
      • MODIS Aqua Daily
      • MODIS Aqua 8-day
      • MODIS Aqua 16-day
      • MODIS Aqua/Terra 16-day
    • Description: Reflectance data in 36 frequency bands: visible to thermal
    • Organization: NASA
    • Website: MODIS Website
    • Google Earth Engine Catalog:
      • Product: MODIS Terra Daily Info
        • LST (MODIS/006/MOD11A1)
        • NDSI Snow Cover (MODIS/006/MOD10A1)
        • MaxFRP (MODIS/006/MOD14A1)
        • All other bands (MODIS/006/MOD09GA)
      • MODIS Terra 8-day Info
        • All bands (MODIS/006/MOD11A2)
      • MODIS Terra 16-day Info
        • 500 m (MODIS/006/MOD13A1)
        • 250 m (MODIS/006/MOD13Q1)
      • MODIS Aqua Daily Info
        • LST (MODIS/006/MYD11A1)
        • NDSI_Snow_Cover (MODIS/006/MYD10A1)
        • MaxFRP (MODIS/006/MYD14A1)
        • All other bands (MODIS/006/MYD09GA)
      • MODIS Aqua 8-day Info
        • All bands (MODIS/006/MYD11A2)
      • MODIS Aqua 16-day Info
        • 500 m (MODIS/006/MYD13A1)
        • 250 m (MODIS/006/MYD13Q1)
        • All bands (MODIS/006/MYD13Q1,MODIS/006/MYD13A1)
      • MODIS Aqua/Terra 16-day Info
        • All bands (MODIS/006/MCD43A4)
    • Spatial resolution: 250-m, 500-m, and 1-km
    • Time Span: 1999 to Present (updated every 1-2 days)
    • Variables:
      • EVI: Enhanced Vegetation Index
      • LST: Daytime Land Surface Temperature
      • NDVI: Normalized Difference Vegetation Index
      • NDSI: Normalized Difference Snow Index
      • NDWI: Normalized Difference Water Index
      • MaxFRP: Maximum Fire Radiative Power
      • BAI: Burned Area Index
    • Methods:
      • MODIS MOD09 User Guide-Version 1.4

    NCEP

    • Description: The National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) jointly produce the NCEP/NCAR Reanalysis data for sea level pressure. The goal of this joint effort is to produce new atmospheric analyses using historical data as well as to produce analyses of the current atmospheric state (Climate Data Assimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present. The data have 6-hour temporal resolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial resolution.
    • Organization: NCEP/NCAR
    • Spatial resolution: 24-km (0.25-deg x 0.25-deg)
    • Time Span: 1948-01-01 to Present (updated in last 5 days)
    • Variables:
      • Sea Level Pressure (slp)
    • Website: NCEP/NCAR Website
    • Google Earth Engine Catalog: NCEP Info (NCEP_RE/sea_level_pressure)
    • References:
      • Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.

    NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)

    • Description: The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) dataset is available as either a daily (NClimGrid-d) or a monthly (NClimGrid-m) dataset. The datasets contain gridded fields and area averages of maximum, minimum, and mean temperature and precipitation amounts for the contiguous United States. NClimGrid consists of gridded fields covering the land area between approximately 24°N and 49°N and between 67°W and 125°W at a resolution of 1/24 of a degree (0.041667°). The primary purpose of these products is to support applications such as drought monitoring that require time series of spatially and/or temporally aggregated gridpoint values. Reliance on single-day values and individual points is discouraged due to the significant uncertainty that is inherent in such a product, as a result of the spatial distribution of the underlying observations, differences in observation time between neighboring stations, and interpolation errors. Spatial and temporal averaging tends to reduce the effect of these uncertainties, and time series of such aggregated values can be shown to be suitable for climatological applications.
    • Organization: NOAA
    • Spatial resolution: 4.6-km (1/24-deg x 1/24-deg)
    • Time Span:
      • Daily: 1951-01-01 to Present (updated every 1-2 weeks)
      • Monthly: 1895-01-01 to Present (updated every month)
    • Variables:
      • Minimum, maximum, mean temperature
      • Precipitation
    • Website: NOAA NCLIMGRID Website
    • Google Earth Engine Catalog: nClimGrid is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Vose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1. [indicate subset used]. NOAA National Centers for Environmental Information. DOI:10.7289/V5SX6B56 [access date].

    NLDAS2 DAILY

    • Description: The Land Data Assimilation System (LDAS) combines multiple sources of observations (such as precipitation gauge data, satellite data, and radar precipitation measurements) to produce estimates of climatological properties at or near the Earth's surface. This dataset is the primary (default) forcing file (File A) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th-degree grid spacing; the temporal resolution is hourly.
    • Organization: NASA
    • Spatial resolution: ~12-km (0.75-deg x 0.75-deg)
    • Time Span: 1979-01-02 to Present (updated every 3-5 days)
    • Variables:
      • Minimum/Maximum/Mean daily air temperature at 2 meters above the surface
      • Total daily Precipitation
      • Mean daily surface downward longwave radiation
      • Mean daily surface downward shortwave radiation
      • Daily grass reference ET
      • Daily alfalfa reference ET
      • Specific Humidity at 2 meters above the surface
      • Wind Speed
      • Mean daily surface pressure
    • Website: NLDAS2 Model Website
    • Google Earth Engine Catalog: Though, NLDAS2 is publicly available on Google Earth Engine, Climate Engine accesses this data as a private GEE asset.
    • References:
      • The data set source should be properly cited when the data are used. A formal reference of the form: \, 2012, last updated 2013: \. NASA/GSFC, Greenbelt, MD, USA, NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Accessed \ at \ is suggested following Parsons et al. (2010), doi:10.1029/2010EO340001.
      • Mitchell et al (2004. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90 https://doi.org/10.1029/2003JD003823
      • Xia et al (2012): Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109. https://doi.org/10.1029/2011JD016048
    • Notes:
      • Daily assets are being generated from the hourly assets available in Earth Engine (source collection ID: "NASA/NLDAS/FORA0125_H002").
      • Reference ET is being computed directly from the daily aggregations (i.e. NOT as the sum of the hourlies).
      • The "day" is defined as 6 UTC to 6 UTC. The start date is lagged by one day because of the 6 UTC start time.
      • The average daily wind speed was computed as the average of the hourly wind speed computed from the wind vector components.

    CHIRPS

    • Datasets:
      • CHIRPS Daily Dataset
      • CHIRPS Pentad Dataset
      • CHIRPS Prelim Pentad Dataset
    • Description:
      • CHIRPS Pentad/Daily: The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates an in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. CHIRPS is produced by the Climate Hazards Center at the University of California Santa Barbara.
      • CHIRPS Prelim Pentad: The Climate Hazards Center InfraRed Precipitation With Station Data-Prelim (CHIRPS-Prelim) is a blend of CHIRPS data with in situ precipitation data to unbias the data and enhance its accuracy. The process of generating CHIRPS- Prelim is similar to the CHIRPS process, with the main difference being its reliance on Global Telecommunication System (GTS) stations only, which are available in near-real time. Blending of CHIRP with GTS-only stations allows for the latency of CHIRPS- Prelim to be <5 days. Note that, in general, the differences in CHIRPS-Prelim and CHIRPS are within acceptable limits, as both data sets share the same climatological mean.
    • Organization: University of California, Santa Barbara (UCSB)
    • Website: CHIRPS Website
    • Google Earth Engine Catalog:
      • CHIRPS Pentad Info (UCSB-CHG/CHIRPS/PENTAD)
      • CHIRPS Daily Info (UCSB-CHG/CHIRPS/PENTAD)
      • CHIRPS Prelim Pentad is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 4.8-km grid (1/20 deg)
    • Time Span:
      • CHIRPS Daily: 1981 to Present (updated monthly)
      • CHIRPS Pentad: 1981 to Present (updated monthly)
      • CHIRPS Prelim Pentad: 2015 to Present (updated weekly)
    • Variables:
      • Precipitation
    • References:
      • Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/10.3133/ds832

    ERA5

    • Description: ECMWF's Copernicus Climate Change Service produces the (ERA5) which is a 30+ year global climate reanalysis dataset. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. Note that the dataset used here is a merger of the Google Earth Engine dataset (up to 7/9/2020) and data to real-time ingested by Climate Engine.
    • Organization: ECMWF
    • Spatial resolution: 24-km (0.25-deg x 0.25-deg)
    • Time Span: 1979-01-02 to Present (updated every 1-2 months)
    • Variables:
      • Minimum/Maximum/Mean Temperature (mean_2m_air_temperature, maximum_2m_air_temperature, minimum_2m_air_temperature)
      • Dewpoint Temperature (dewpoint_2m_temperature)
      • Precipitation (total_precipitation)
      • Sea Level Pressure (mean_sea_level_pressure)
      • Eastward/Northward Wind Component (u_component_of_wind_10m,v_component_of_wind_10m)
    • Website: ERA5 Website
    • Google Earth Engine Catalog: ERA5 Info (ECMWF/ERA5/DAILY)
    • References:
      • Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), (date of access), https://cds.climate.copernicus.eu/cdsapp#!/home

    ERA5 Ag

    • Description: Daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models.
      Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.
    • Organization: ECMWF
    • Spatial resolution: 9600 m (1/10-deg)
    • Time Span: 1979-01-01 to Present (updated daily with 7 day lag time)
    • Variables:
      • Minimum/Maximum/Mean Temperature (mean_2m_air_temperature, maximum_2m_air_temperature, minimum_2m_air_temperature)
      • Dewpoint Temperature (dewpoint_2m_temperature)
      • Precipitation (total_precipitation)
      • Snow (Snow_Depth, SWE)
      • Wind speed (wind_speed)
      • Vapour pressure (vap)
      • Downward Solar Radiation (srad)
    • Website: ERA5 Ag Webpage
    • References:
      • Copernicus Climate Change Service (C3S) (2017): ERA5 Ag: Agrometeorological indicators from 1979 to present derived from reanalysis. Copernicus Climate Change Service Climate Data Store (CDS), (date of access), https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview

    ERA5 Land

    • Description: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.
    • Organization: ECMWF
    • Spatial resolution: 11.1-km (0.125-deg x 0.125-deg)
    • Time Span: 1963-07-11 to Present (updated every 1-2 months)
    • Variables:
      • Mean Temperature (temperature_2m)
      • Dewpoint Temperature (dewpoint_2m_temperature)
      • Precipitation (total_precipitation_sum)
      • Wind Speed (u_component_of_wind_10m,v_component_of_wind_10m)
    • Website: ERA5 Land Website
    • Google Earth Engine Catalog: ERA5 Land Info (ECMWF/ERA5_LAND/DAILY_RAW)
    • References:
      • Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (), doi:10.24381/cds.68d2bb30

    MERRA2

    • Description: NASAs Global Modeling and Assimilation Office (GMAO) produces the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) which is a 30+ year global climate reanalysis dataset.
    • Organization: NASA GMAO
    • Spatial resolution: ~50-km (0.5-deg x 0.625-deg)
    • Time Span: 1980-12-31 to Present (updated every 1-2 months)
    • Variables:
      • Minimum/Maximum Temperature (T2MMIN/T2MMAX)
      • Precipitation (PRECTOTLAND)
    • Website: MERRA2 Website
    • Google Earth Engine Catalog: MERRA2 is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • MERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim., doi: 10.1175/JCLI-D-16-0758.1

    MERRA2 FWI

    • Description: The Global Fire WEather Database (GFWED) integrates different weather factors influencing the likelihood of a vegetation fire starting and spreading. It is based on the Fire Weather Index (FWI) System, the most widely used fire weather system in the world. The FWI System was developed in Canada, and is composed of three moisture codes and three fire behavior indices. The moisture codes capture the moisture content of three generalized fuel classes and the behavior indices reflect the spread rate, fuel consumption and intensity of a fire if it were to start. Details on the development and testing of GFWED can be found in Field et al. (2015) and evaluation of GFWED products in Field (2020a). Applications of the FWI System can be found in Taylor and Alexander (2006) and technical descriptions are provided by van Wagner (1987) and Dowdy et al. (2009).
    • Organization: NASA
    • Spatial resolution: ~50-km (0.5-deg x 0.625-deg)
    • Time Span: 1980-04-02 to Present (updated daily)
    • Variables:
      • Fire Weather Index (FWI)
    • Website:
      • MERRA2 Website
      • GFWED Website
    • Google Earth Engine Catalog: MERRA2 FWI is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Field, R.D., A.C. Spessa, N.A. Aziz, A. Camia, A. Cantin, R. Carr, W.J. de Groot, A.J. Dowdy, M.D. Flannigan, K. Manomaiphiboon, F. Pappenberger, V. Tanpipat, and X. Wang, 2015: Development of a global fire weather database. Nat. Hazards Earth Syst. Sci., 15, 1407-1423, doi:10.5194/nhess-15-1407-2015.
      • MERRA-2 Overview: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Ronald Gelaro, et al., 2017, J. Clim., doi: 10.1175/JCLI-D-16-0758.1

    CFS Reanalysis

    • Datasets:
      • CFS Reanalysis
    • Description: NCEP Climate Forecast System Reanalysis dataset
    • Organization: NOAA NWS National Centers for Environmental Prediction (NCEP)
    • Website: CFSR Website
    • Google Earth Engine Catalog: CFSR Info (NOAA/CFSV2/FOR6H)
    • Spatial resolution:
      • After 2011: 19.2-km grid (1/5-deg)
      • Before 2011: 28.8-km grid (3/10-deg)
    • Time Span: 1979 to Present (updated every day)
    • Variables:
      • Minimum/Maximum/Mean Temperature
      • Minimum/Maximum/Mean Specific Humidity
      • Precipitation
      • Wind Speed and Components
      • Latent/Sensible Heat
      • Downward/Upward Shortwave/Longwave and Net Radiation
      • Soil Moisture
      • Potential Evapotranspiration
      • Surface Geopotential Height
    • Notes:
      • There was a grid change in 1999(See article) which means that there is a discontinuity in the grid cells prior to 1999 and after 1999. This grid change can be seen as grid artifacts on maps in Climate Engine when you take an average over a year range that includes years from before 1999 and after 1999.

    CFS gridMET

    • Dataset:
      • CFS gridMET
    • Description: Surface meteorological forecast dataset of 48-ensemble members of CFS forecasts bias corrected to gridMET statistics.
    • Organization: University of California Merced (UCM)
    • Documentation: Google Doc Documentation of CFS gridmet methods
    • Website: UCM Applied Climate Lab Website
    • Data: CFS gridMET Catalog on THREDDS Server
    • Google Earth Engine Catalog:
      • CFS GRIDMET is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: Next 1-28 days (updated every day)
    • Variables:
      • Minimum/Maximum Temperature
      • Precipitation
      • Specific Humidity
      • Downward Solar Radiation
      • Near Surface Wind Velocity
      • ASCE Grass Reference Evapotranspiration
      • Energy Release Component(model-G)
      • Burning Index (model-G)
      • 100-hr & 1000-hr Fuel Moisture (model-G)
      • Vapor Pressure Deficit
      • Evaporative Demand Drought Index
    • References:
      • Abatzoglou J. T. "Development of gridded surface meteorological data for ecological applications and modelling". International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract)
      • Saha, Suranjana and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015.1057. doi: 10.1175/2010BAMS3001.1

    CFS gridMET Daily

    • Dataset:
      • CFS gridMET Daily
    • Description: Surface meteorological forecast dataset of 48-ensemble members of CFS forecasts bias corrected to gridMET statistics.
    • Organization: University of California Merced (UCM)
    • Documentation: Google Doc Documentation of CFS gridmet methods
    • Website: UCM Applied Climate Lab Website
    • Data: CFS gridMET Catalog on THREDDS Server
    • Google Earth Engine Catalog:
      • CFS GRIDMET is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: Next 1-7 days (updated every day)
    • Variables:
      • Minimum/Maximum Temperature
      • Precipitation
      • Specific Humidity
      • Downward Solar Radiation
      • Near Surface Wind Velocity
      • ASCE Grass Reference Evapotranspiration
      • Energy Release Component(model-G)
      • Burning Index (model-G)
      • 100-hr & 1000-hr Fuel Moisture (model-G)
      • Vapor Pressure Deficit
    • References:
      • Abatzoglou J. T. "Development of gridded surface meteorological data for ecological applications and modelling". International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract)
      • Saha, Suranjana and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015.1057. doi: 10.1175/2010BAMS3001.1

    FRET

    • Dataset:
      • FRET
    • Description: The National Weather Service is now producing Forecast Reference Crop Evapotranspiration (FRET), a forecast estimate of the amount of evapotranspiration for a well- watered reference crop (grass or alfalfa) under prescribed conditions for a 24 hour period. Weekly FRET forecast calculations and NLDAS derived reference crop ET Climatology and departure from normal are available as well. The Forecast Reference Evapotranspiration (FRET) are for a short canopy (or 12cm grasses). The short canopy ET values are calculated using the Penman-Monteith Reference Evapotranspiration Equations, adopted by the Environmental Water Resources Institute - American Society of Civil engineers (ASCE-EWRI, 2004), and the National Weather Service forecast of temperatures, relative humidity, wind, and cloud cover. This product will be issued daily by 8 am local time, year round.
    • Organization: National Weather Service (NWS)
    • Website:
      • FRET Conference Poster
      • Weather.gov website on FRET
    • Data: National Weather Service Data Viewer
    • Google Earth Engine Catalog:
      • FRET is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 4-km grid (1/24-deg)
    • Time Span: Next 1-7 days (updated every hour)
    • Variables:
      • ASCE Grass Reference Evapotranspiration
    • References:

    USGS MODIS ET

    • Datasets:
      • USGS MODIS ET: SSEBop Dekadal (~10-day)
      • USGS MODIS ET: SSEBop Monthly
      • USGS MODIS ET: SSEBop Annual
    • Description: Remote sensing derived evapotranspiration dataset based on MODIS-thermal imagery and global weather datasets. Climate Engine is using version 5 of the global ET product.
    • Organization: United States Geological Survey (USGS)
    • Website: FEWS NET page
    • Google Earth Engine Catalog: USGS MODIS ET is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 1-km grid (1/96-deg)
    • Time Span: 2003 to Present (updated every 10-12 days)
    • Variables:
      • ETa: Actual Evapotranspiration (mm)
    • Methods:
      • Actual ET (ETa) is produced using the operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for the period 2003 to present.
      • The SSEBop setup is based on the Simplified Surface Energy Balance (SSEB) approach (Senay et al., 2007, 2011) with unique parameterization for operational applications using a principle that is comparable to psychometry. A comprehensive evaluation of the model was conducted by Velpuri et al. (2013).
      • The global ET is derived from the integration of MODIS-based (Aqua) land surface temperature, maximum air temperature from WorldClim, and reference ET derived from global data assimilation systems(GDAS).
    • References:
      • Senay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, https://doi.org/10.5066/P9OUVUUI. Publication: https://www.mdpi.com/1424-8220/20/7/1915
      • Senay, G. B., Budde, M. E., & Verdin, J. P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4), 606-618.
      • Senay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000.
      • Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET, Remote Sensing of Environment, 139, 35-49, (Abstract and Article)

    MODIS TERRANET ET

    • Datasets: MODIS MOD16 L4 Tile 500M Gridded PRODUCT: ET 8-day composite
    • Description: The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite product produced at 500 meter pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.
    • Organization: NASA LP DAAC at the USGS EROS Center
    • Website:
      • Documentation Webpage
      • Users Guide
    • Google Earth Engine Catalog:
      • MODIS MOD16 ET Info (MODIS/006/MOD16A2)
    • Spatial resolution: 500-m grid (1/48-deg)
    • Time Span: 2001 to Present (updated every week)
    • Variables:
      • ETa: Total Evapotranspiration
      • PET: Total Potential Evapotranspiration
    • References:
      • Please visit LP DAAC 'Citing Our Data' page for information on citing LP DAAC datasets.

    Monitoring Trends in Burn Severity (MTBS)

    • Datasets: MTBS Thematic Burn Severity
    • Description: Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern United States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. To produce the data Landsat images are analyzed through a standardized and consistent methodology, generating products at a 30-meter resolution dating back to 1984.
    • Organization: USFS Geospatial Technology and Applications Center (GTAC)
    • Website: MTBS Website
    • Google Earth Engine Catalog: USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1
    • Spatial resolution: 30 m
    • Time Span: 1984-01-01 to present (updated every year)
    • Variables:
      • Thematic Burn Severity
    • References:
      • Eidenshink, J., Schwind, B., Brewer, K. et al. A Project for Monitoring Trends in Burn Severity. fire ecol 3, 3–21 (2007). https://doi.org/10.4996/fireecology.0301003

    PML V2 ET

    • Datasets: MODIS PML V2 Gridded ET 8-day composite
    • Description: The Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include evapotranspiration (ET), its three components, and gross primary product (GPP) at 500m and 8-day resolution during 2002-2017 and with spatial range from -60°S to 90°N. The major advantages of the PML_V2 products are: coupled estimates of transpiration and GPP via canopy conductance (Gan et al., 2018; Zhang et al., 2019), partitioning ET into three components: transpiration from vegetation, direct evaporation from the soil and vaporization of intercepted rainfall from vegetation (Zhang et al., 2016).
    • Organization: PML V2
    • Website:
      • PML V2 gitHub code
    • Google Earth Engine Catalog:
      • PML_V2: Coupled Evapotranspiration and Gross Primary Info(CAS/IGSNRR/PML/V2)
    • Spatial resolution: 500-m grid (1/48-deg)
    • Time Span: 2002 to 2017 (currently not updated)
    • Variables:
      • GPP: Gross primary product
      • Ec: Vegetation transpiration
      • Es: Soil evaporation
      • Ei: Interception from vegetation canopy
      • Actual Evapotranspiration - computed in Climate Engine as the sum of Ec+Ei+Es
    • References:
      • Zhang, Y., Kong, D., Gan, R., Chiew, F.H.S., McVicar, T.R., Zhang, Q., and Yang, Y., 2019. Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182, https://doi.org/10.1016/j.rse.2018.12.031
      • Gan, R., Zhang, Y.Q., Shi, H., Yang, Y.T., Eamus, D., Cheng, L., Chiew, F.H.S., Yu, Q., 2018. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology, https://doi.org/10.1002/eco.1974
      • Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y.Y., Miralles, D.G., Pan, M., 2016. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124. https://doi.org/10.1038/srep19124

    OISST

    • Datasets:
      • Optimum Interpolation Sea Surface Temperature(OISST)
    • Description: The NOAA Optimum Interpolation Sea Surface Temperature is an analysis constructed by combining observations from different platforms (satellites, ships, buoys and Argo floats) on a regular global grid. A spatially complete SST map is produced by interpolating to fill in gaps. The methodology includes bias adjustment of satellite and ship observations (referenced to buoys) to compensate for platform differences and sensor biases.
    • ersion 5.2 Sea Surface Temperature dataset (PFV52) is a collection of global, twice-daily 4km sea surface temperature data.
    • Organization: NOAA
    • Website: OISST Technical Specifications
    • Google Earth Engine Catalog: OISST Info (NOAA/CDR/OISST/V2_1)
    • Spatial resolution: 24-km grid (1/4 deg)
    • Time Span: 1981-08-24 to Present (updated in the last 5 days)
    • Variables:
      • Sea Surface Temperature
    • References:
      • Richard W. Reynolds, Viva F. Banzon, and NOAA CDR Program (2008): NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5SQ8XB5 [access date].

    OPENET

    • Datasets:OpenET CONUS Ensemble Monthly Evapotranspiration v2.0
    • Description: The OpenET dataset includes satellite-based data on the total amount of water that is transferred from the land surface to the atmosphere through the process of evapotranspiration (ET). OpenET provides ET data from multiple satellite-driven models, and also calculates a single "ensemble value" from the model ensemble. The models currently included in the OpenET model ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop. The OpenET ensemble ET value is calculated as the mean of the ensemble after filtering and removing outliers using the median absolute deviation approach. All models currently use Landsat satellite data to produce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per pixel). The monthly ET dataset provides data on total ET by month as an equivalent depth of water in millimeters.
    • Organization: OpenET, Inc
    • Website: OpenET page
    • Google Earth Engine Catalog: OpenET Info (OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0)
    • Spatial resolution: 30-m grid
    • Time Span: 2016 to Recent Year (updated every 1 year)
    • Variables:
    • Methods:OpenET page
    • References:
      • Melton, F., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J., Kilic, A., Senay, G., volk, J., Hain, C., Johnson, L., Ruhoff, A., Blanenau, P., Bromley, M., Carrara, W., Daudert, B., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Halverson, G., Hansen, J., Harding, J., Kang, Y., Ketchum, D., Minor, B., Morton, C., Ortega-Salazar, S., Ott, T., Ozdogon, M., Schull, M., Wang, T., Yang, Y., Anderson, R., 2021. OpenET: Filling a Critical Data Gap in Water Management for the Western United States. Journal of the American Water Resources Association, 2021 Nov 2.

    PERSIANN-CDR

    • Datasets:
      • PERSIANN-CDR Dataset
    • Description: PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.
    • Organization: NOAA NCDC
    • Website: NCEI PERSIANN Website
    • Google Earth Engine Catalog: PERSIANN-CDR Info (NOAA/PERSIANN-CDR)
    • Spatial resolution: 24-km grid (1/4-deg)
    • Time Span: Jan 1, 1983 - Present
    • Variables:
      • Precipitation
    • References:
      • Publications using this dataset should also cite the following journal article: Ashouri H., K. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily Precipitation Climate Data Record from Multi-Satellite Observations for Hydrological and Climate Studies. Bull. Amer. Meteor. Soc., doi: https://doi.org/10.1175/BAMS-D-13-00068.1.
      • Sorooshian, Soroosh; Hsu, Kuolin; Braithwaite, Dan; Ashouri, Hamed; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V51V5BWQ [access date].

    Rangeland Analysis Platform (RAP)

    • Datasets:
      • RAP Vegetation Cover
      • RAP Herbaceous Production
    • Description:
      • RAP Vegetation Cover, version 3.0: This datasets consists of gridded fractional estimates of plant functional groups for rangelands in the continental United States. The estimates are produced at 30-meter spatial resolution for each year between 1984–present. The six plant functional groups are Annual Forbs and Grasses, Perennial Forbs and Grasses, Shrubs, Trees, Litter, and Bare Ground. Cover values are reported as percentages on a pixel-by-pixel basis. The estimates were produced using a temporal convolutional network using field measures of plant functional groups collected by the Natural Resources Conservation Service Natural Resources Inventory (NRI) program, the Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) program, and the National Park Service Northern Colorado Plateau Network (NCPN) alongside spatially continuous earth observations from Landsat TM, ETM+, and OLI Collection 2.
      • RAP Herbaceous Production, version 3.0: This dataset consists of gridded estimates of herbaceous aboveground biomass, partitioned into vegetation types for annual forbs and grasses and perennial forbs and grasses. The estimates are produced at 30m spatial resolution from 1986-present. Estimates are provided annually and at 16-day intervals. Values are reported in terms of net primary productivity which can be converted to pounds per acre of new growth of aboveground biomass using the function in the Google Earth Engine script below– estimates do not reflect standing biomass from previous years. Estimates are calculated using a light use efficiency model (to estimate net primary production in terms of carbon) which is then allocated to aboveground and belowground pools (based on mean annual temperature) and further converted to biomass using a carbon-to-dry matter ratio.
    • Organization: University of Montana, Numerical Terradynamic Simulation Group
    • Website: RAP Website
    • Google Earth Engine Catalog and Downloads:
      • RAP Earth Engine Asset Endpoints and Sample Scripts
      • Download Vegetation Cover rasters
      • Download Herbaceous Production rasters
    • Spatial resolution: 30-meter
    • Time Span:
      • RAP Vegetation Cover: 1986 to Present (updated annually)
      • RAP Herbaceous Biomass: 1986 to Present (updated annually)
    • Variables:
      • RAP Vegetation Cover
        • Perennial forbs and grasses
        • Annual forbs and grasses
        • Shrubs
        • Trees
        • Bare ground
        • Litter
      • RAP Herbaceous Biomass
        • Total production (sum of perennial herbaceous and annual herbaceous plant production)
        • Annual herbaceous plant production
        • Perennial herbaceous plant production
    • References:
      • RAP Vegetation Cover
        • Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, S.D., T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution https://doi.org/10.1111/2041-210X.13564
      • RAP Herbaceous Biomass
        • Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W. Lankston, and B.W. Allred. Annual and 16-day rangeland production estimates for the western United States. Rangeland Ecology and Management https://doi.org/10.1016/j.rama.2021.04.003
        • Robinson, N. P., M. O. Jones, A. Moreno, T. A. Erickson, D. E. Naugle, and B. W.Allred. 2019. Rangeland productivity partitioned to sub-pixel plant functional types. Remote Sensing 11:1427. http://dx.doi.org/10.3390/rs11121427

    Sentinel 2 Remote Sensing

    • Datasets:
      • Sentinel 2 Top of Atmosphere (TOA)
      • Sentinel 2 Surface Reflectance (SR)
    • Description: Reflectance data from the Copernicus Sentinel-2 satellite is available for top-of-atmosphere(TOA) and for the earth surface reflectance(SR) in visible, near, and short wave infrared bands. Several vegetation indices are constructed from these bands such as NDVI, EVI, NDSI, NDWI.
    • Organization: European Space Agency (ESA)
    • Website: Sentinel Website
    • Google Earth Engine Catalog: the following Google Earth Engine products are used in Climate Engine
      • Sentinel 2 TOA Info (COPERNICUS/S2)
      • Sentinel 2 SR Info (COPERNICUS/S2_SR)
    • Spatial resolution: 10-m, 20-m, and 60-m
    • Time Span:
      • Sentinel 2 TOA: 2015 to Present (updated every 5/10 days)
      • Sentinel 2 SR: 2017 to Present (updated every 5/10 days)
    • Variables:
      • EVI: Enhanced Vegetation Index
      • NDVI: Normalized Difference Vegetation Index
      • NDSI: Normalized Difference Snow Index
      • NDWI: Normalized Difference Water Index
    • Methods:
    • ClimateEngine applies a cloud mask to the Sentinel 2 data. The cloud masking attempts to remove medium and high confidence snow, shadow and cirrus clouds using the BQA quality band provided in the Sentinel 2 GEE collection.
      • Handbook
      • Cloud mask: cloud masks are applied for dense clouds (opaque) and cirrus clouds (Technical Guide)

    Sentinel 5P

    • Datasets:Sentinel-5 Precursor (5P)
    • Description: Sentinel-5 Precursor is an Earth observation satellite developed by ESA dedicated to monitoring our atmosphere. The main objective of the Copernicus Sentinel-5P mission is to perform atmospheric measurements with high spatio-temporal resolution, to be used for air quality, ozone & UV radiation, and climate monitoring & forecasting.
    • Organization: European Space Agency (ESA)
    • Website: Sentinel 5P Website
    • Google Earth Engine Catalog: the following Google Earth Engine products are used in Climate Engine
      • Sentinel 5P Offline Methane Info (COPERNICUS/S5P/OFFL/L3_CH4)
    • Spatial resolution: 1-km (1/100-deg)
    • Time Span:
      • Sentinel 5P Methane: Feb 8, 2019 to Present (updated 2-3 days)
    • Variables:
      • Methane concentration: column averaged dry air mixing ratio of methane in units of ppbV (parts per billion by Volume)

    SNODAS

    • Description: The National Operational Hydrologic Remote Sensing Center (NOHRSC) at NOAA produces the SNOw Data Assimilation System (SNODAS) which is a modeling and data assimilation system developed to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis.
    • Organization: NOAA
    • Spatial resolution: ~1-km (1/120-deg)
    • Time Span: 2003-10-01 to Present (updated daily)
    • Variables:
      • Snow Water Equivalent (SWE)
      • Snow Depth
    • Website: SNODAS Website
    • Google Earth Engine Catalog: SNODAS is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Barrett, Andrew. 2003. National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special Report 11. Boulder, CO USA: National Snow and Ice Data Center. 19 pp.

    GEPS

    • Global Ensemble Prediction System Forecast Dataset:
      • GEPS 2-Week Forecasts
      • GEPS 4-Week Forecasts
    • Description: The Global Ensemble Prediction System (GEPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 16 days into the future (up to 32 days once a week on Thursdays at 00UTC). The GEPS produces different outlooks (scenarios) to estimate the forecast uncertainties due the nonlinear (chaotic) behaviour of the atmosphere. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions, choice of physics parametrization as well as stochastic perturbations (physical tendencies and kinetic energy). A control member that is not perturbed is also available.
    • Organization: Canadian Meteorological Centre
    • Documentation: GEPS Documentation
    • Website: GEPS Website
    • Google Earth Engine Catalog:
      • GEPS is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 55-km grid (1/2-deg)
    • Time Span:
      • (GEPS 2-Week Forecasts) Next 1-15 days (updated every week)
      • (GEPS 4-Week Forecasts) Next 1-31 days (updated every day)
    • Variables:
      • Minimum/Maximum/Mean Daily Temperature
      • Precipitation

    GPM DAILY

    • Datasets: Global Precipitation Measurement
    • Description: Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world.
    • Organization: NASA
    • Website: NASA GPM website
    • Google Earth Engine Catalog: GPM on Earth Engine Catalog
    • Spatial resolution: 11-km (1/10-deg)
    • Time Span: Jun 1, 2000 to Present
    • Variables:
      • Precipitation (Calibrated): daily data is derived from 30-minute data
    • References:
      • Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7. Abstract, Link
      • Information on Algorithm

    TRMM DAILY (Derived from 3-hourly)

    • Datasets: TRMM/3B42
    • Description: TRMM Precipitation Estimates
    • Organization: NASA GSFC
    • Website: NASA TRMM website
    • Google Earth Engine Catalog: TRMM on Earth Engine Catalog
    • Spatial resolution: 28-km (1/4-deg)
    • Time Span: Jan1 1, 1998 to Present
    • Variables:
      • Precipitation
    • References:
      • Information on Algorithm
      • File Specifications

    FLDAS

    • Datasets:
      • FLDAS Dataset
    • Description: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System
    • Organization: NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)
    • Website: NASA FLDAS Website
    • Google Earth Engine Catalog: FLDAS Info (NASA/FLDAS/NOAH01/C/GL/M/V001)
    • Spatial resolution: 9.6-km grid (1/10-deg)
    • Time Span: Jan 1, 1982 - Present (updated around the 20th of the month (i.e., data for Jan 2020 will be available on or around Feb 20, 2020.)
    • Variables:
      • Evapotranspiration
      • Surface Runoff
      • Total Runoff
      • Surface Soil Moisture (0-10cm)
      • Root Zone Soil Moisture (0-100cm)
      • Snow depth
      • Snow water equivalent
    • References:
      • If you use these data in your research or applications, please include a reference in your publication(s) similar to the following example: Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/5NHC22T9375G
      • McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C.D., & Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012.

    GLDAS

    • Datasets:
      • GLDAS Dataset
    • Description: Global Land Data Assimilation System (GLDAS) ingests satellite and ground-based observational data products. Using advanced land surface modeling and data assimilation techniques, it generates optimal fields of land surface states and fluxes. The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) precipitation fields (Adler et al., 2003), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields which became available for March 1, 2001 onwards.
    • Organization: NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)
    • Website: NASA GLDAS Website
    • Google Earth Engine Catalog: GLDAS Info (NASA/GLDAS/V021/NOAH/G025/T3H)
    • Spatial resolution: 24-km grid (1/4-deg)
    • Time Span: Jan 1, 2000 - Present
    • Variables:
      • Evapotranspiration
      • Surface Runoff
      • Total Runoff
      • Surface Soil Moisture (0-10cm)
      • Root Zone Soil Moisture (0-100cm)
      • Snow depth
      • Snow water equivalent
    • References:
      • Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3), 381-394, 2004. http://www.jstor.org/stable/26216951

    ESI

    • Description: The Evaporative Stress Index (ESI) is produced by the NOAA Center for Satellite Applications and Research (STAR) and USDA-ARS Hydrology and Remote Sensing Laboratory. The Evaporative Stress Index (ESI) is a thermal indicator of anomalous ET conditions that can be used for drought monitoring. The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast- response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of “flash drought”, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion.
    • Organization: NOAA STAR and USDA ARS HRSL
    • Spatial resolution: 4-km (1/24-deg)
    • Time Span: 2001-01-02 to Present (updated every month)
    • Variables:
      • Evaporative Stress Index (4-week and 12-week time scales)
    • Website: ESI Website
    • Google Earth Engine Catalog: ESI is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset by scraping data from this site.
    • References:
      • Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, and J. R. Mecikalski, 1997: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195-216.
      • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007a: A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: I. Model formulation. J. Geophys. Res., 112, D10117, doi:10110.11029/12006JD007506.
      • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007b: A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:11110.11029/12006JD007507.
      • Anderson, M. C., C. R. Hain, B. Wardlow, J. R. Mecikalski, and W. P. Kustas (2011), Evaluation of a drought index based on thermal remote sensing of evapotranspiration over the continental U.S., J. Climate, 24, 2025-2044.
      • McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. AMS Eighth conf. on Applied Climatology, Anaheim, CA, 179-184.
      • McKee, T. B., N. J. Doesken, and J. Kleist, 1995: Drought monitoring with multiple time scales. AMS Ninth conf. on Applied Climatology, Dallas, TX, 233-236.
      • Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Met., 77, 263-293.
      • Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteorol. Soc., 83, 1181-1190.

    US Drought Outlook

    • Description: The United States Drought Outlook raster dataset is produced by the National Weather Service Climate Prediction Center. It is released on the last day of each month and provides informant on drought outlook for the following month.
      • NoData Value=-9999 = no data
      • 0 = no data
      • 1 = drought removal likely
      • 2= drought remains but improves
      • 3= drought development likely
      • 4= drought persists
    • Organization: National Weather Service Climate Prediction Center
    • Spatial resolution: 0.5-km (1/100-deg)
    • Time Span: 2013-07-31 to Present (updated last day of each month)
    • Variables:
      • Drought Outlook Class
    • Website: US Drought Outlook Website
    • Google Earth Engine Catalog: This US Drought Outlook raster dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset by scraping data from here.

    Canadian Drought Outlook

    • Description: The Canadian Drought Outlook raster dataset is produced by the Agriculture and Agri-Food Canada. The Canadian Drought Outlook predicts whether drought across Canada will emerge, stay the same or get better over the target month. In calculating the outlook, consideration is given to Agroclimate indices, such as the Standard Precipitation Index (SPI), the Standard Precipitation Evaporation Index (SPEI), and the Palmer Drought Severity Index (PDSI). The drought outlook is issued on the first Thursday of each calendar month and is valid for 32 days from that date.
      • NoData Value=-9999 = no data
      • 0 = no data
      • 1 = drought removal
      • 2= drought improves
      • 3= drought develops
      • 4= drought persists
      • 5= drought worsens
    • Organization: Agriculture and Agri-Food Canada
    • Spatial resolution: ~0.8-km (1/100-deg)
    • Time Span: 2021-06-01 to Present (updated first week of each month)
    • Variables:
      • Drought Outlook Class
    • Website: Canadian Drought Outlook Website
    • Google Earth Engine Catalog: This Canadian Drought Outlook raster dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset by scraping data from here.
    • References:
      • Agriculture and Agri-Food Canada, 2021, “Canadian Drought Outlook”, Agroclimate, Geomatics and Earth Observation Division, Science and Technology Branch.

    Satellite Precipitation - CMORPH

    • Description: The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and reprocessed using the the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis. Data is reprocessed on a global grid with daily temporal resolution.
    • Organization: NOAA Climate Prediction Center
    • Spatial resolution: 25-km (1/2-deg x 1/2-deg)
    • Time Span: 1998-01-01 to Present (updated daily)
    • Variables:
      • Precipitation
      • Standardized Precipitation Index - Climate Engine calculates this on-the-fly from Precipitation
    • Website: NOAA CMORPH Website
    • Google Earth Engine Catalog: This dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset]. NOAA National Centers for Environmental Information. https://doi.org/10.25921/w9va-q159 [access date]

    NOAA Unified Gauge-Based Analysis of Daily Precipitation

    • Description: This data set is part of products suite from the CPC Unified Precipitation Project(UPP) that are underway at NOAA Climate Prediction Center (CPC). The primary goal of the project is to create a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. There are two datasets in Climate Engine from this product suite:
      • Global dataset: a dataset covering the globe at 1/2-deg resolution.
      • CONUS dataset: The gauge analysis here covers the conterminous United States(CONUS) on a fine-resolution and is quantitatively consistent with that covering the global land on a coarser resolution.
      See the CPC's data docs for more details or email them.
    • Organization: NOAA Physical Sciences Laboratory
    • Spatial resolution: Global: 55-km (1/2-deg x 1/2-deg), CONUS: 28km (1/4-deg x 1/4-deg)
    • Time Span: Global: 1979-01-01 to Present, CONUS: 1948-01-01 to Present (updated daily)
    • Variables:
      • Minimum/maximum/mean temperature(Global only)
      • Precipitation (CONUS and Global)
    • Website: NOAA CPC Global Precipitation Website
    • Google Earth Engine Catalog: These two datasets are not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • References:
      • (Interpolation algorithm) Xie_et_al_2007_JHM_EAG.pdf Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang (2007), A gauge-based analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607. 626.
      • (Gauge Algorithm Evaluation) Chen_et_al_2008_JGR_Gauge_Algo.pdf Chen, M., W. Shi, P. Xie, V. B. S. Silva, V E. Kousky, R. Wayne Higgins, and J. E. Janowiak (2008), Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.
      • (Construction of the Daily Gauge Analysis) Chen_et_al_2008_Daily_Gauge_Anal.pdf Chen, M., P. Xie, and Co-authors (2008), CPC Unified Gauge-based Analysis of Global Daily Precipiation, Western Pacific Geophysics Meeting, Cairns, Australia, 29 July - 1 August, 2008.

    USDM

    • Description: The US Drought Monitor (USDM) raster dataset is produced by the National Centers for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration's (NOAA) National Integrated Drought Information System (NIDIS). This dataset is a gridded version of the US Drought Monitor (USDM) produced by the National Drought Mitigation Center (NDMC) where for each 2.5-km gridcell, the value is given by the current USDM drought classification for that region is:
      • NoData Value=-9999 = no drought or wet
      • 0 = abnormal dry
      • 1 = moderate drought
      • 2= severe drought
      • 3= extreme drought
      • 4= exceptional drought
    • Organization: NCEI-NIDIS
    • Spatial resolution: 2.5-km (0.025 deg)
    • Time Span: 2001-01-04 to Present (updated every 7 days)
    • Variables:
      • USDM Drought Classification
    • Website: USDM Website
    • Google Earth Engine Catalog: This USDM raster dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset by scraping data from here.
    • References:
      • The U.S. Drought Monitor is jointly produced by the National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. Map courtesy of NDMC.

    NADM

    • Description: The North American Drought Monitor (NADM) raster dataset is produced by the National Centers for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration's (NOAA) National Integrated Drought Information System (NIDIS). This dataset is a gridded version of the North American Drought Monitor (NADM) produced by Canadian, Mexican and US authors where for each 2.5-km gridcell, the value is given by the current NADM drought classification for that region is:
      • NoData Value=-9999 = no drought or wet
      • 0 = abnormal dry
      • 1 = moderate drought
      • 2= severe drought
      • 3= extreme drought
      • 4= exceptional drought
    • Organization: NCEI-NIDIS
    • Spatial resolution: 2.5-km (0.025 deg)
    • Time Span: 2001-01-04 to Present (updated every 7 days)
    • Variables:
      • NADM Drought Classification
    • Website: NADM Website
    • Google Earth Engine Catalog: This NADM raster dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset by scraping data from here.
    • References:
      • Heim, Jr., R. R., 2002. A review of Twentieth-Century drought indices used in the United States. Bulletin of the American Meteorological Society, 83, 1149-1165.
      • Lawrimore, J., et al., 2002. Beginning a new era of drought monitoring across North America. Bulletin of the American Meteorological Society, 83, 1191-1192.
      • Lott, N., and T. Ross, 2000. NCDC Technical Report 2000-02, A Climatology of Recent Extreme Weather and Climate Events. [Asheville, N.C.]: National Climatic Data Center.
      • Svoboda, M., et al., 2002. The Drought Monitor. Bulletin of the American Meteorological Society, 83, 1181-1190.

    MODIS Burned Area

    • Datasets:
      • MODIS Burned Area
    • Description: The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500m MODIS Surface Reflectance imagery coupled with 1km MODIS active fire observations. The algorithm uses a burn sensitive vegetation index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred, with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells.
    • Organization: NASA
    • Website:
      • General Documentation
      • User's Guide
      • Methods Documentation
    • Google Earth Engine Catalog:
      • MODIS Burned Area Info (MODIS/061/MCD64A1)
    • Spatial resolution: 500-m, and 1-km
    • Time Span: 2000 to Present (updated every 2 months)
    • Variables:
      • BurnDate: Burn day of year. Possible values: 0 (unburned), 1-366 (approximate Julian day of burning).
      • FirstDay: First day of the year of reliable change detection
      • LastDay: Last day of the year of reliable change detection
    • Citation:
      • Please visit LP DAAC 'Citing Our Data' page for information on citing LP DAAC datasets.

    WRC

    • Datasets:
      • Wildfire Risk to Communities (WRC)
    • Description: The Wildfire Risk to Communities dataset was created by USDA Forest Service to help assess risk to homes, businesses, and other valued resources. The dataset contains nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. In situ risk (risk at the location where the adverse effects take place on the landscape) are modeled using the large fire simulation system (FSim) and LANDFIRE fuel loading datasets from 2014. The original data at 250m has been upsampled to 30m for this dataset on Climate Engine.
    • Organization: USDA Forest Service
    • Website: WRC Publication Details
    • Google Earth Engine Catalog: WRC is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: 30m
    • Time Span: 2014 (Static). This dataset is based on data from 2014 LANDFIRE data. It is a static dataset and it is unknown when it will be updated to reflect more recent conditions.
    • Variables: (See definitions of these under Metrics information)
      • Burn Probability
      • Conditional Flame Length
      • Conditional Risk to Potential Structures
      • Exposure Type
      • Flame Length Exceedance Probability – 4 ft
      • Flame Length Exceedance Probability – 8 ft
      • Risk to Potential Structures
      • Wildfire Hazard Potential index
    • References:
      • Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data Archive. Updated 25 November 2020. https://doi.org/10.2737/RDS-2020-0016 (Publication Details)

    Earthquake Hazard

    • Datasets:
      • Earthquake hazard dataset
    • Description: These data represent the chance of experiencing potentially damaging ground shaking for fixed ground shaking levels that corresponds with Modified Mercalli Intensity (MMI) equal to VI, in 100 years. The values are obtained by averaging the probability of experiencing MMI VI based on a peak ground acceleration, and the probability of experiencing MMI VI based on 1.0-second spectral acceleration. Ground motions are amplified using topographic, slope-based Vs30 values (Wald and Allen, 2007). Validity begins in 2018.
    • Organization: USGS
    • Website:
    • Google Earth Engine Catalog: This earthquake risk dataset is not publicly available on Google Earth Engine yet. Climate Engine accesses this data as a private GEE asset.
    • Spatial resolution: ~11 km (1/10-deg)
    • Time Span: 2018 (Static).
    • Variables:
      • Percent chance of a damaging earthquake
    • References:
      • Rukstales, K.S., and Petersen, M.D., 2019, Data Release for 2018 Update of the U.S. National Seismic Hazard Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9WT5OVB.
      • Wald, D.J., and Allen, T.J., 2007, Topographic slope as a proxy for seismic site conditions and amplification, Bulletin of the Seismological Society of America, 97(5), 1379-1395

    Metrics

    Climate, vegetation, and drought can be monitored many different ways through ground observations, gridded weather and climate data, and airborne and satellite remote sensing. Access to Google Earth Engine’s satellite image and meteorological collections allow for efficient near real-time drought monitoring through statistical analyses of surface temperature, vegetation, precipitation, snow cover, surface water, soil moisture, evapotranspiration, and simple water balances.

    • Precipitation
    • SPI
    • Water Balance
    • PDSI
    • Evapotranspiration
    • ETo/ETr
    • EDDI
    • Vegetation
    • NDVI
    • EVI
    • ERC
    • Water/Snow
    • NDWI
    • NDSI
    • Drought Blends
    • Short & Long Term Blends
    • Wildfire Risk
    • Wildfire Risk to Communities

    Standardized Precipitation Index (SPI)

    The Standardized Precipitation Index (SPI; McKee et al. 1993) is based solely on accumulated precipitation. Accumulated precipitation over different precipitation can be used to detect precipitation deficits and drought over short (i.e. weeks) and long (years) timescales. The intensity of the drought at different time scales can be measured using a traditional drought metric of the Standardized Precipitation Index(SPI). A SPI value near 0 represents precipitation near normal conditions, while positives or negatives values represent precipitation amounts above or below normal conditions. SPI is approximately the number of standard deviations the precipitation amount (accumulated over a specified time scale, i.e. 3-months, 6-month, 12-month, or 24-month) is above the mean precipitation amount. SPI values below -2 represent drought conditions. The main limitation of the SPI is that it is based entirely on precipitation and ignores other variables that affect atmospheric water demand such as solar radiation, temperature, humidity, and windspeed. While SPI has some limitations for detecting different types of drought, it is useful for evaluating precipitation anomalies at different time scales and complements other drought indices.

    Climate Engine computes SPI (and EDDI/SPI) using a non-parametric standardized probability based method. Plotting positions are used to obtain probabilities and then converted to SPI values using an inverse-normal distribution.

    Hobbins, M., A. Wood, D.J. McEvoy, J. Huntington, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part I – Linking Drought Evolution to Variations in Evaporative Demand. Journal of Hydrometeorology. 17, 1745-1761, doi: 10.1175/JHM-D-15-0121.1

    McEvoy, D.J., J.L. Huntington, M. Hobbins, A. Wood, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part II – CONUS-wide Assessment Against Common Drought Indicators. Journal of Hydrometeorology. 17, 1763-1779, doi: 10.1175/JHM-D-15-0122.1.

    Palmer Drought Severity Index (PDSI)

    One of the first and most highly used drought indices is the Palmer drought severity index (PDSI; Palmer 1965), which is based on a simplified soil water balance and is a measure of the departure of average soil moisture conditions. Instead of the typical parameterization of PDSI using Thornthwaite temperature only based potential ET, we utilize more physically based Penman-Monteith reference ET, which is a function of solar radiation, temperature, humidity and windspeed. A PDSI value between -.5 and 0.5 represents near normal soil moisture conditions, with positive/negative values representing wet/dry conditions. The magnitude of PDSI gives an indication as to the severity of the departure from normal conditions. PDSI> 4 represents very wet conditions, while PDSI<-4 represents an extreme drought.

    Palmer, W. C., 1965, Meteorological drought. U.S. Department of Commerce Weather Bureau Research Paper 45, 58 pp.

    Reference Evapotranspiration (ETo)

    Reference evapotranspiration represents ET from a well-watered idealized reference surface and is a function of solar radiation, air temperature, humidity, and windspeed. Reference ET is often considered an upper limit on actual ET. Actual ET is usually estimated by scaling reference ET downward based on estimates of the fraction of reference ET (EToF) based on remotely sensed or simulated soil and vegetation moisture conditions, and vegetation type and phenology. ETo estimates in CLIM Engine are derived from the Penman-Monteith model (ASCE-EWRI, 2005; Allen et al., 1998) under ambient meteorological and radiative conditions derived from meteorological reanalyses, gridMET (Abatzoglou, 2013). ETo assumes a reference surface of short grass (0.12 m high), while ETr assumes a reference surface of tall grass (or alfalfa).

    Abatzoglou, J. T., 2013, Development of gridded surface meteorological data for ecological applications and modeling. Int. J. Climatol., 33, 121–131

    Allen, R.G., L.S. Pereira, D. Raes, and M. Smith, 1998. Crop Evapotranspiration: Guidelines for Computing Crop Requirements. Irrigation and Drainage Paper No. 56, United Nations Food and Agricultural Organization (FAO), Rome, Italy.

    ASCE-EWRI, 2005. The ASCE Standardized Reference Evapotranspiration Equation. ASCE-EWRI Standardization of Reference Evapotranspiration Task Committee Report. American Society of Civil Engineers, Reston, Virginia.

    Evaporative Demand Drought Index (EDDI)

    Standardization of ETo similar to SPI has shown to be useful for drought monitoring and analysis of atmospheric land surface coupling and feedbacks. One example is the Evaporative Demand Drought Index (EDDI; Hobbins et al., 2016; McEvoy et al., 2016), which is showing promise as a leading indicator of agricultural drought at time-frames pertaining to both flash (i.e., fast-developing) and extended droughts. For time periods of interest, if ETo is higher than normal it is usually indicates dry and hot conditions, whereas lower than normal ETo usually indicates moist and cool conditions. ETo responds positively to both flash droughts and sustained droughts. ETo rises in response to drought via the complementary relationship, where drought typically increases air temperature and lowers humidity levels due to the lack of precipitation and subsequent lack of actual ET. ET based drought metrics complement other in drought metrics.

    Climate Engine computes EDDI (and SPEI) using a non-parametric standardized probability based method. Plotting positions are used to obtain probabilities and then converted to EDDI and SPEI values using an inverse-normal distribution.

    Hobbins, M., A. Wood, D.J. McEvoy, J. Huntington, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part I – Linking Drought Evolution to Variations in Evaporative Demand. Journal of Hydrometeorology. 17, 1745-1761, doi: 10.1175/JHM-D-15-0121.1

    McEvoy, D.J., J.L. Huntington, M. Hobbins, A. Wood, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part II – CONUS-wide Assessment Against Common Drought Indicators. Journal of Hydrometeorology. 17, 1763-1779, doi: 10.1175/JHM-D-15-0122.1.

    Normalized Difference Vegetation Index (NDVI)

    The shortage of water available to vegetation in a drought limits the growth and productivity of vegetation. Chlorophyll, which is the pigment in plant leaves, strongly absorbs red light (from 0.6 to 0.7 µm) for photosynthesis. The cell structure of the leaves strongly reflects near-infrared light (from 0.7 to 1.1 µm). The magnitude of absorption and reflection of red and near-infrared light is strongly a function of leaf area and vegetation vigor. Satellite imagery has long been used to evaluate differences in plant reflectance and to determine their spatial distribution. A common satellite image index of vegetation vigor is the Normalized Difference Vegetation Index (NDVI) (Huete et al., 1985; Jackson and Huete, 1991), which ranges from -1 to 1, with ~ 0.5 to 1 representing high vegetation vigor. Effects of drought can be visualized through computing time series and spatial anomalies of NDVI.

    Huete, A. R., Jackson, R. D., and Post, D. F. (1985), Spectral response of a plant canopy with different soil backgrounds, Remote Sens. Environ. 17:37-53.

    Jackson, R. D., and Huete, A. R. (1991), Interpreting vegetation indices, J. Preventative Vet. Med. 11:185-200.

    Enhanced Vegetation Index (EVI)

    The Enhanced Vegetation Index is a common vegetation index that was developed to optimize the vegetation signal and improve the sensitivity to improve vegetation monitoring in through a de-coupling of the canopy background signal and atmosphere influences (Liu and Huete, 1995). Like NDVI drought can be visualized through computing time series and spatial anomalies of EVI.

    Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33, 457−465

    A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(2002) 195-213.

    Energy Release Component(ERC)

    The Energy Release Component (ERC) is an index which is related to the potential heat released at the flaming front of a fire measured in units of available energy per square foot. This index can be converted to the available energy per unit area within the flaming front at the head of a fire (in units of BTU/sq. ft) by multiplying the index by a factor of 25. ERC is a commonly used fire danger index by fire management in the United States for tracking the fire season and serves as a guide for fire suppression and fuel treatment operations. The ERC is one of the outputs of the National Fire Danger Rating System (NFDRS, Bradshaw et al., 1983) that represents the cumulative drying effect of daily meteorology on both live fuel moistures and 100 and 1000-hour dead fuel moistures and is considered a build-up index as it’s values are carried over from day to day. As such, ERC generally tracks within season moisture specific to fuels that can potentially carry fire and thus represents concurrent moisture stress rather than longer-time drought stress like PDSI. ERC is most sensitive to variations in relative humidity and precipitation, but does not incorporate the influence of wind speed. We use a common fuel model (model G, or dense confer stand with heavy litter accumulation) in ERC calculations for consistency across space as well as its frequent use by regional fire management. ERC values are best viewed as either percentiles or anomalies from the historic value for individual locations as a value of ERC=60 can represents very different relative conditions from place to place.

    Bradshaw, L.S., R.E. Burgan, J.D. Cohen, and J.E. Deeming. 1983. The 1978 National Fire Danger Rating System: Technical Documentation. USDA Forest Service; Intermountain Forest and Range Experiment Station, General Technical Report INT-169, Ogden, Utah. 44 pp.

    Normalized Difference Water Index (NDWI)

    The normalized difference water index can be utilized for evaluating vegetation liquid water contents or water inundated areas (Gao, 1996). NDWI is useful for evaluating reflectance from vegetation canopies that have similar scattering properties, but slightly different liquid water absorption due to canopy water content. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies and open water areas. The common range of NDWI for green vegetation is -0.1 to 0.4 with 0.4 indicating high vegetation water content.

    Climate Engine provides a couple different versions of NDWI, utilizing normalized differences of different bands, which can be more useful for different applications:
  • NDWI (NIR/SWIR1) = (NIR-SWIR1)/(NIR+SWIR1)
  • NDWI (GREEN/NIR) = (GREEN-NIR)/(GREEN+NIR)
  • NDWI (GREEN/SWIR1) = (GREEN-SWIR1)/(GREEN+SWIR1)
  • NDWI (GREEN/SWIR2) = (GREEN-SWIR2)/(GREEN+SWIR2)
  • NDWI (SWIR1/GREEN) = (SWIR1-GREEN)/(SWIR1+GREEN)

  • where NIR = near infra-red band, GREEN = green band, SWIR1 = 1.55 - 1.75 micrometer band, SWIR2 =2.08 - 2.35 micrometer band.

    Gao, B.C., 1996, NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.

    Normalized Difference Snow Index (NDSI)

    Knowledge on the snow extent, depth, and water content is important for water resource management, planning, and forecasting. Monitoring of the snow extent using satellite imagery is useful for understanding snow depletion and recession rates, evaluating snow extent relative to long term average conditions, and is a useful drought metric. Snow cover area is often estimated using the Normalized Difference Snow Index (NDSI) (Crane and Anderson, 1984; Dozier, 1984). Snow is highly reflective in the visible part of the electromagnetic spectrum and highly absorptive in the near-infrared or short-wave infrared band of the spectrum. Reflectance of clouds is usually high in both the visible and infrared bands, allowing for separation of snow and clouds. NDSI usually ranges from -5 to 1, with ~0.5 to 1 representing snow cover. Time series and anomaly maps of NDSI clearly show changes in snow cover for a region. Positive anomalies indicate increased snow cover, whereas negative anomalies indicate decreased snow cover relative to average conditions.

    Crane, R. G., and Anderson, M. R., 1984, Satellite discrimination of snow/cloud surfaces. International Journal of Remote Sensing, 5(1), 213 ­223.

    Dozier, J., 1984, Spectgal signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment, 28.9-22.

    Short & Long Term Drought Blends

    A blend of different drought indices for short and long term time scales can be useful to understand short and long term drought for a region. The experimental short and long term objective blends produced by the Climate Prediction Center (CPC) are an example of blends being produced as a weighting of percentiles for different drought metrics from past data where the weights are based on expert judgment from drought experts. The blends produced by Climate Engine are instead produced as a weighting of standardized indices for the same drought metrics. The metrics that go into the experimental CPC blend are:
  • Palmer-Z Index (Z)
  • Palmer Drought Severity Index (PDSI)
  • Standardized Precipitation Index (SPI)( 30-day, 90-day, 180-day, 1-year, 2-year and 5-year)
  • Palmer Hydrological Drought Index (PDHI)
  • Soil Moisture from NOAH (SM-NOAH)

  • In Climate Engine, we are providing blends that are instead a weighting of the standardized indices coming from drought indices calculated from the gridMET data product (also in Climate Engine and based on 1981-2016). The weightings are the same as the experimental CPC blend with some differences:
  • the weightings for SM-NOAH (soil moisture from NOAH) and PHDI (Palmer Hydrological Drought Index) are added in with the weights for PDSI. Only PDSI is used in the blend construction to represent soil moisture.
  • in the construction, the Palmer drought indices (i.e Z and PDSI) will be divided by 2 to put them on roughly the same scale as the standardized indices
  • the colors and bins used to visualize the drought blends will be the US Drought monitor colors with non-linear standardized index bins.

  • The precise details of the construction of the blends is:

    Short-term Blend= 0.2 *(PDSI/2) + 0.2 * SPI30d + 0.25 * SPI90d + 0.35 * (Z/2)
    where
  • PDSI = Palmer Drought Severity Index
  • Z = Palmer's Z-Index
  • SPI30d = 30-day Standardized Precipitation Index (SPI)
  • SPI90d = 90-day Standardized Precipitation Index (SPI)

  • Long-term Blend= 0.35 *(PDSI/2) + 0.15 * SPI180d + 0.2 * SPI1y + 0.2 *SPI2y + 0.1 * SPI5y
    where
  • PDSI = Palmer Drought Severity Index
  • SPI180d = 180-day Standardized Precipitation Index (SPI)
  • SPI1y = 1-year Standardized Precipitation Index (SPI)
  • SPI2y = 2-year Standardized Precipitation Index (SPI)
  • SPI5y = 5-year Standardized Precipitation Index (SPI)
  • Wildfire Risk to Communities

    The Wildfire Risk to Communities dataset was created by USDA Forest Service to help assess risk to homes, businesses, and other valued resources. The dataset contains nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. In situ risk (risk at the location where the adverse effects take place on the landscape) are modeled using the large fire simulation system (FSim) and LANDFIRE fuel loading datasets from 2014.

    This dataset contains the following variables:
    • Burn Probability - The annual probability of wildfire burning in a specific location.
    • Conditional Flame Length - Most likely flame length at a given location if a fire occurs, based on all simulated fires; an average measure of wildfire intensity.
    • Conditional Risk to Potential Structures - The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there.
    • Exposure Type - Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.
    • Flame Length Exceedance Probability – 4 ft - Probability of having flame lengths greater than 4 feet if a fire occurs, on a 0 to 1 scale; indicates the potential for moderate to high wildfire intensity.
    • Flame Length Exceedance Probability – 8 ft - Probability of having flame lengths greater than 8 feet if a fire occurs, on a 0 to 1 scale; indicates the potential for high wildfire intensity.
    • Risk to Potential Structures - A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed.
    • Wildfire Hazard Potential index - An index that quantifies the relative potential for wildfire that may be difficult to control, used as a measure to help prioritize where fuel treatments may be needed.

    References

    References for Figures/Data from our Site

    If you wish to reference a figure generated from this website or processed data downloaded from this site, please use the following reference for the website:

    Climate Engine. (year). Desert Research Institute and University of Idaho. Accessed on (date).http://climateengine.org.

    Soon, we will have a reference publication:

    Huntington, J.L. et al. Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data. submitting to Bulletin of the American Meteorological Society (BAMS) (in preparation).

    Tutorials

    • Using Custom Region

    Tutorial on Creating a Google Fusion Table from a KML File

    ClimateEngineTutorial_ImportCustomRegions from Climate Engine on Vimeo.

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    Contact Our Team

    Justin Huntington

    Justin Huntington

    Research Professor, Hydrology
    Desert Research Institute
    Britta Daudert

    Britta Daudert

    Research Scientist
    Desert Research Institute
    Charles Morton

    Charles Morton

    Research Scientist
    Desert Research Institute
    Dan McEvoy

    Dan McEvoy

    Research Professor, Climatology
    Desert Research Institute
    John Abatzoglou

    John Abatzoglou


    Associate Professor, Management of Complex Systems
    University of California, Merced
    Katherine Hegewisch

    Katherine Hegewisch


    Project Scientist
    University of California, Merced
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