National Renewable Energy Laboratory

GeoServer Web Map Service

swera_us_wind_hi_res swera_us_wind_hi_res clim_cdd10_nasa_low
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Interface
Web Service, OGC Web Map Service 1.3.0
Keywords
WFS, WMS, GEOSERVER
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No INSPIRE Extended Capabilities (including service language support) given. See INSPIRE Technical Guidance - View Services for more information.
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National Renewable Energy Laboratory (unverified)

Contact information:

Anthony Lopez

National Renewable Energy Laboratory

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80401 Golden,, USA

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No INSPIRE Extended Capabilities (including service metadata) given. See INSPIRE Technical Guidance - View Services for more information.

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A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSS

Available map layers (73)

Cooling Degree Days Polygon Global 1 Degree NASA 2007 (clim_cdd10_nasa_low)

Cooling Degree Days above 10° C (degree days)The monthly accumulation of degrees when the daily mean temperature is above 10° C.NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly Average & Annual Sum (July 1983 - June 2005) Parameter: Cooling Degree Days Above 10 degrees C (degree days)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data. These data are regional averages; not point data.Created: December 10, 2007See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Heating Degree Days Polygon Global 1 Degree NASA 2007 (clim_hdd18_nasa_low)

Heating Degree Days below 18° C (degree days)The monthly accumulation of degrees when the daily mean temperature is below 18° C.NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly Average & Annual Sum (July 1983 - June 2005)Parameter: Heating Degree Days Below 18 degrees C (degree days)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: December 10, 2007See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Atmospheric Pressure Polygon Global 1 Degree NASA 2007 (clim_ps_nasa_low)

Atmospheric Pressure (kPa)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Atmospheric Pressure (kPa)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: May 13, 2008See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at the Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Units:kilopascal (kPa). Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Relative Humidity Polygon Global 1 Degree NASA 2007 (clim_rh10m_nasa_low)

Relative Humidity at 10 m Above The Surface Of The Earth (%)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Relative Humidity at 10 m Above The Surface Of The Earth (%)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: December 10, 2007See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Air Temperature Polygon Global 1 Degree NASA 2007 (clim_temp_nasa_low)

Air Temperature at 10 m Above The Surface Of The Earth (deg C)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Air Temperature at 10 m Above The Surface Of The Earth (deg C)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: November 27, 2007See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at the Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Earth Skin Temperature Polygon Global 1 Degree NASA 2007 (clim_tskin_nasa_low)

Earth Skin Temperature (° C) NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Earth Skin Temperature (deg C)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: November 27, 2007See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Solar DNI Polygon N.Africa to E.China 10km DLR 2003 (dni_dlr_high)

SRID 4326 of two available coordinate systems. Data of high resolution (10kmx10km) Direct Normal Irradiance (DNI) for the years 2000, 2002 and 2003. The data are available for monthly and annual sums stored in an ESRI-Shapefile. Please read the country report for additional background information. Data included for Bangladesh, China, Ethiopia, Ghana, Kenya, Nepal, Sri Lanka, and United Arab Emirates. Units: KWh/m sq. per day. Source: DLR - Deutsches Zentrum für Luft- und Raumfahrt

Solar DNI Polygon N.Africa to E.China 10km DLR 2003 (dni_dlr_high_900913)

SRID 900913 of two available coordinate systems. Data of high resolution (10kmx10km) Direct Normal Irradiance (DNI) for the years 2000, 2002 and 2003. The data are available for monthly and annual sums stored in a ESRI-Shapefile. Please read the country report for additional background information. Data included for Bangladesh, China, Ethiopia, Ghana, Kenya, Nepal, Sri Lanka, and United Arab Emirates. Units: KWh/m sq. per day. Source: DLR - Deutsches Zentrum für Luft- und Raumfahrt

Solar DNI Polygon Turkey 10km GeoModel 2010 (dni_geomodel_high)

SRID 4326 of two available coordinate systems. Developed by NREL and the U.S. Trade and Development Agency, this geographic toolkit that allows users to relate the renewable energy resource (solar and wind) data to other geographic data, such as land use, protected areas, elevation, etc. The GsT was completely redesigned and re-released in November 2010 to provide a more modern, easier-to-use interface with considerably faster analytical querying capabilities. The revised version of the Geospatial Toolkit for Turkey is available using the following link: http://www.nrel.gov/international/downloads/gst_turkey.exe units: kWh/m sq. per day, DNI 10km Resolution by GeoModelSource: GeoModelAccess shared data and shapefiles.

Solar DNI Polygon Turkey 10km GeoModel 2010 (dni_geomodel_high_900913)

SRID 900913 of two available coordinate systems. Developed by NREL and the U.S. Trade and Development Agency, this geographic toolkit that allows users to relate the renewable energy resource (solar and wind) data to other geographic data, such as land use, protected areas, elevation, etc. The GsT was completely redesigned and re-released in November 2010 to provide a more modern, easier-to-use interface with considerably faster analytical querying capabilities. The revised version of the Geospatial Toolkit for Turkey is available using the following link: http://www.nrel.gov/international/downloads/gst_turkey.exe units: kWh/m sq. per day, DNI 10km Resolution by GeoModelSource: GeoModelAccess shared data and shapefiles.

Solar DNI Polygon Brazil 10km INPE (dni_inpe_high)

SRID 4326 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km units:KWh/m sq. per day, Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar DNI Polygon Brazil 10km INPE (dni_inpe_high_900913)

SRID 900913 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km units:KWh/m sq. per day, Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar DNI Polygon Brazil 40km INPE (dni_inpe_mod)

SRID 4326 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km units:KWh/m sq. per day, Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar DNI Polygon Brazil 40km INPE (dni_inpe_mod_900913)

SRID 900913 of two available coordinate systems. Normal direct solar radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar DNI Polygon Global 1 Degree NASA 2008 (dni_nasa_low)

Direct Normal Irradiance (kWh/m^2/day)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Jan 2008)22-year Monthly & Annual Average (July 1983 - June 2005) Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Solar DNI Polygon Multiple Countries 40km NREL (dni_nrel_mod)

Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. Countries included in dataset include: Africa, Bangladesh, Brazil, Caribbean, Central America, China, East Asia, Ethiopia, Ghana, Kenya, Mexico, Nepal, South America, Sri Lanka, and the United States. Units: kWh/m^2/day Source: U.S. National Renewable Energy Laboratory (NREL)

Solar DNI Polygon Multiple Countries 40km NREL (dni_nrel_mod_900913)

Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. Countries included in dataset include: Africa, Bangladesh, Brazil, Caribbean, Central America, China, East Asia, Ethiopia, Ghana, Kenya, Mexico, Nepal, South America, Sri Lanka, and the United States. Units: kWh/m^2/day Source: U.S. National Renewable Energy Laboratory (NREL)

Solar DNI Polygon Multiple Countries 10km SUNY (dni_suny_high)

High resolution monthly average direct normal irradiance (DNI) solar resource for Afghanistan, Bhutan, Central America, Cuba, India, Pakistan, United States. units:KWh/m sq. per day, Source: SUNY Albany

Solar DNI Polygon Multiple Countries 10km SUNY (dni_suny_high_900913)

High resolution monthly average direct normal irradiance (DNI) solar resource for Afghanistan, Bhutan, Central America, Cuba, India, Pakistan, United States. units:KWh/m sq. per day, Source: SUNY Albany

Solar GHI Polygon Multiple Countries 10km DLR 2000-2003 (ghi_dlr_high)

Data of high resolution (10kmx10km) Direct Normal Irradiance (DNI) for Bangladesh, China, Ethiopia, Ghana, Kenya, Nepal, Sri Lanka, United Arab Emirates. Years collected between 2000 to 2003, varying for each individual country included. units:KWh/m sq. per day, Source: DLR - Deutsches Zentrum für Luft- und Raumfahrt

Solar GHI Polygon Multiple Countries 10km DLR 2000-2003 (ghi_dlr_high_900913)

Data of high resolution (10kmx10km) Direct Normal Irradiance (DNI) for Bangladesh, China, Ethiopia, Ghana, Kenya, Nepal, Sri Lanka, United Arab Emirates. Years collected between 2000 to 2003, varying for each individual country included. units:KWh/m sq. per day, Source: DLR - Deutsches Zentrum für Luft- und Raumfahrt

Solar GHI Polygon Turkey 10km GeoModel (ghi_geomodel_high)

SRID 4326 of two available coordinate systems. Solar GHI 10 km resolution by GeoModel for Turkey. Units: kWh/m sq. per day. Source: GeoModel

Solar GHI Polygon Turkey 10km GeoModel (ghi_geomodel_high_900913)

SRID 900913 of two available coordinate systems. Solar GHI 10 km resolution by GeoModel for Turkey. Units: kWh/m sq. per day. Source: GeoModel

Solar GHI Polygon Brazil 10km INPE 2009 (ghi_inpe_high)

SRID 4326 of two available coordinate systems. Global horizontal solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km. The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources in countries from European Union. In the second phase, the solar flux estimates provided by the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory. Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar GHI Polygon Brazil 10km INPE 2009 (ghi_inpe_high_900913)

SRID 900913 of two available coordinate systems. Global horizontal solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km. The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources in countries from European Union. In the second phase, the solar flux estimates provided by the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory. Source: INPE (National Institute for Space Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar GHI Polygon S.America 40km INPE 2009 (ghi_inpe_mod)

SRID 4326 of two available coordinate systems. Global Horizontal Solar Radiation for South America with 40km resolution. Units: KWh/m sq. per day, Source: INPE (National Institute for Spatial Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar GHI Polygon S.America 40km INPE 2009 (ghi_inpe_mod_900913)

SRID 900913 of two available coordinate systems. Global Horizontal Solar Radiation for South America with 40km resolution. Units: KWh/m sq. per day, Source: INPE (National Institute for Spatial Research) and LABSOLAR (Laboratory of Solar Energy/Federal University of Santa Catarina) - Brazil

Solar GHI Polygon Global 1 Degree NASA 2008 (ghi_nasa_low)

Global Horizontal IrradianceNASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Jan 2008)22-year Monthly & Annual Average (July 1983 - June 2005) Parameter: Insolation Incident On A Horizontal Surface (kWh/m^2/day) Internet: http://eosweb.larc.nasa.gov/sse/ Note 1: SSE Methodology & Accuracy sections online Note 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data.Created: March 12, 2008See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format. Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE)

Solar GHI Polygon Multiple Countries 40km NREL 2006 (ghi_nrel_mod)

SRID 4326 of two available coordinate systems. Monthly average solar resource for horizontal flat-plate collectors for Africa, Bangladesh, Brazil, Caribbean, Central AMerica, Eas Asia, Ethiopia, Ghana, Kenya, Mexico, Nepal, South America, and Sri Lanka. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Source: NREL

Solar GHI Polygon Multiple Countries 40km NREL 2006 (ghi_nrel_mod_900913)

SRID 900913 of two available coordinate systems. Monthly average solar resource for horizontal flat-plate collectors for Africa, Bangladesh, Brazil, Caribbean, Central AMerica, Eas Asia, Ethiopia, Ghana, Kenya, Mexico, Nepal, South America, and Sri Lanka. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Source: NREL

Solar GHI Polygon Multiple Countries 10km SUNY (ghi_suny_high)

SRID 4326 of two available coordinate systems. Monthly global horizontal irradiance for Afghanistan, Bhutan, Central America, Cuba, India, Pakistan, and the United States. Cell size 10km x 10km. Units: kWh/m sq. per day Source: SUNY Albany

Solar GHI Polygon Multiple Countries 10km SUNY (ghi_suny_high_900913)

SRID 900913 of two available coordinate systems. Monthly global horizontal irradiance for Afghanistan, Bhutan, Central America, Cuba, India, Pakistan, and the United States. Cell size 10km x 10km. Units: kWh/m sq. per day Source: SUNY Albany

Wind Polygon Multiple Countries 200m NREL (global_wind_grid_no_us)

SRID 4326 of two available SRIDs.. Wind resource polygon at 200 meter resolution for Mexico, Cuba, Dominican Republic, Belize, Guatemala, Honduras, El Salvador, Nicaragua, Ghana, China, Mongolia, Pakistan, Afghanistan, some parts of Russia, and some offshore resource availability for the countries above.

Wind Polygon Multiple Countries 200m NREL (global_wind_grid_no_us_900913)

SRID 900913 of two available SRIDs.. Wind resource polygon at 200 meter resolution for Mexico, Cuba, Dominican Republic, Belize, Guatemala, Honduras, El Salvador, Nicaragua, Ghana, China, Mongolia, Pakistan, Afghanistan, some parts of Russia, and some offshore resource availability for the countries above.

Wind Speed 30km Offshore NOAA 2006, 2008, 2009 (global_wind_offshore)

SRID 4326 of two available coordinate systems. GIS data for offshore wind speed (meters/second). Specified to Exclusive Economic Zones (EEZ).Wind resource based on NOAA blended sea winds and monthly wind speed at 30km resolution, using a 0.11 wind sheer to extrapolate 10m - 90m. Annual average >= 10 months of data, no nulls. Units:m/s at 90m ASL. Source: National Renewable Energy Laboratory (NREL)

Wind Speed 30km Offshore NOAA 2006, 2008, 2009 (global_wind_offshore_900913)

SRID 900913 of two available coordinate systems. GIS data for offshore wind speed (meters/second). Specified to Exclusive Economic Zones (EEZ).Wind resource based on NOAA blended sea winds and monthly wind speed at 30km resolution, using a 0.11 wind sheer to extrapolate 10m - 90m. Annual average >= 10 months of data, no nulls. Units:m/s at 90m ASL. Source: National Renewable Energy Laboratory (NREL)

Solar DNI Polygon India 10km NREL 2013 (india_dni_ann_month)

This map depicts model estimates of annual average direct normal irradiance (DNI) at 10 km resolution based on hourly estimates of radiation over 10 years (2002-2011). The inputs are visible imagery from geostationary satellites, aerosol optical depth, water vapor, and ozone. Units: kWh/m sq. per day, Source: NREL

Solar GHI Polygon India 10km NREL 2013 (india_ghi_ann_month)

This map depicts model estimates of annual average global horizontal irradiance (GHI) at 10 km resolution based on hourly estimates of radiation over 10 years (2002-2011). The inputs are visible imagery from geostationary satellites, aerosol optical depth, water vapor, and ozone. Units: kWh/m sq. per day, Source: NREL

International Parks Polygon Global WDPA 2006 (international_poly_wdpa2006)

SRID 4326 of two available coordinate systems. International Parks of the world as designated by the World Database on Protected Areas (WDPA).

International Parks Polygon Global WDPA 2006 (international_poly_wdpa2006_900913)

SRID 900913 of two available coordinate systems. International Parks of the world as designated by the World Database on Protected Areas (WDPA).

National Parks Polygon Global WDPA 2006 (national_iucn1to6_poly_wdpa2006)

National Parks of the world as designated by the World Database on Protected Areas.

National Parks (Other) Polygon Global WDPA 2006 (national_otherareas_poly_wdpa2006)

Other National Parks not found in the National Parks Polygon Global WDPA 2006 layer as designated by the World Database on Protected Areas.

Administrative Boundaries Polygon Global ESRI 2008 (ref_admin)

World Administrative Units represents the boundaries for the first-level administrative units of the world.

World Cities Points Global ESRI 2002 (ref_cities)

World Cities provides a base map layer of the cities for the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities.

Country Boundaries Polygon Global ESRI 2008 (ref_countries)

World Countries (Generalized) represents generalized boundaries for the countries of the world as they existed in January 2008.

Detailed Lakes Polygon Global Natural Earth (ref_detailed_lakes)

Primarily derived from World Data Bank 2 with numerous reservoir additions from other sources, primarily imagery. The diminishing areal extent of the Aral Sea and Lake Chad derives from recent satellite imagery. Ranked by relative importance, coordinating with river ranking. Includes name attributes. Natural Earth generated this data primarily using the CIA's World Databank 2 datasets which were completed in 1977.

Detailed Rivers Polyline Global Natural Earth (ref_detailed_rivers)

Rivers primarily derived from World Data Bank 2. Double line rivers in WDB2 were digitized to created single line drainages. All rivers received manual smoothing and position adjustments to fit shaded relief generated from SRTM Plus elevation data, which is more recent and (presumably) more accurate. Lake centerlines obtained by manually drawing connecting segments in reservoirs. When available, Admin 0 and 1 political boundaries in reservoirs serve as the lake centerlines. Ranked by relative importance. Includes name and line width attributes for creating tapered drainages. Natural Earth generated this data primarily using the CIA's World Databank 2 datasets which were completed in 1977.

Major Lakes Polygon Global ESRI 2008 (ref_lakes)

World Lakes represents the major lakes and inland seas within the world.

Major Rivers Polygon Global ESRI 2008 (ref_rivers)

World Rivers represents the major rivers within the world.

Wind Grid 200m-25km United States NREL (swera_us_wind_hi_res)

Wind resource of the United States. Resolution of dataset ranges from 200m up to 25km depending on location. Source: NREL

Solar TILT Polygon Turkey 10km GeoModel 2011 (tilt_geomodel_high)

SRID 4326 of two available coordinate systems. TILT 10km Resolution by GeoModel for Turkey. Units: kWh/m sq. per day, Source: GeoModel

Solar TILT Polygon Turkey 10km GeoModel 2011 (tilt_geomodel_high_900913)

SRID 900913 of two available coordinate systems. TILT 10km Resolution by GeoModel for Turkey. Units: kWh/m sq. per day, Source: GeoModel

Solar TILT Polygon 10km Brazil INPE 2008 (tilt_inpe_high)

SRID 4326 of two available coordinate systems. Latitude tilted solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km. (Purpose): The BRASIL-SR model and the SPRING software (both developed by INPE -National Institute for Space Research) were used to produce the dataset and SHAPE files (Supplemental Information): The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources incountries from European Union. In the second phase, the solar flux estimates providedby the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory

Solar TILT Polygon 10km Brazil INPE 2008 (tilt_inpe_high_900913)

SRID 900913 of two available coordinate systems. Latitude tilted solar radiation in kWh/m2/day for 1 year organized into cells with 10km x 10km. (Purpose): The BRASIL-SR model and the SPRING software (both developed by INPE -National Institute for Space Research) were used to produce the dataset and SHAPE files (Supplemental Information): The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources incountries from European Union. In the second phase, the solar flux estimates providedby the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory

Solar TILT Polygon 40km Brazil and South America INPE 1995-2005 (tilt_inpe_mod)

SRID 4326 of two available coordinate systems. Photosynthetically active radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km. (Purpose): The BRASIL-SR model and the SPRING software (both developed by INPE - National Institute for Space Research) were used to produce the dataset and SHAPE files (Supplemental Information): The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources in countries from European Union. In the second phase, the solar flux estimates provided by the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory.

Solar TILT Polygon 40km Brazil and South America INPE 1995-2005 (tilt_inpe_mod_900913)

SRID 900913 of two available coordinate systems. Photosynthetically active radiation in kWh/m2/day for 1 year organized into cells with 40km x 40km. (Purpose): The BRASIL-SR model and the SPRING software (both developed by INPE - National Institute for Space Research) were used to produce the dataset and SHAPE files (Supplemental Information): The assessment of reliability levels of the BRASIL-SR model were performed through the evaluation of the deviations shown by the estimated values for solar radiation flux vis-à-vis the values measured at the surface (ground truth). This evaluation was done in two phases. The first phase consisted in an inter-comparison between the core radiation transfer models adopted by the SWERA Project to map the solar energy in the various countries participating in the project. The HELIOSAT model took part in this phase like benchmark due to its employment to map solar energy resources in countries from European Union. In the second phase, the solar flux estimates provided by the BRASIL-SR model were compared with measured values acquired at several solarimetric stations spread along the Brazilian territory.

Solar TILT Polygon Global NASA 2008 (tilt_nasa_low)

SRID 4326 of two available coordinate systems. Latitude Tilt Irradiance NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Jan 2008) 22-year Monthly & Annual Average (July 1983 - June 2005). Parameter: Latitude Tilt Radiation (kWh/m2/day) Internet: http://eosweb.larc.nasa.gov/sse/ Note 1: SSE Methodology & Accuracy sections online Note 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are regional averages; not point data. Created: March 17, 2008 See the NASA Surface meteorology and Solar Energy (SSE) web site at http://eosweb.larc.nasa.gov/sse/. The source data was downloaded from the SSE website at Data Retrieval: Meteorology and Solar Energy > Global data sets as text files. The tabular data was then converted to the shapefile format.

Solar TILT Polygon Multiple Countries 40km NREL 1985-1991 (tilt_nrel_mod)

SRID 4326 of two available coordinate systems. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Solar TILT Polygon Multiple Countries 40km NREL 1985-1991 (tilt_nrel_mod_900913)

SRID 900913 of two available coordinate systems. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Solar TILT Polygon Multiple Countries 10km SUNY 1998-2005 (tilt_suny_high)

SRID 4326 of two available coordinate systems. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 10 km by 10 km in size. The solar resource value is represented as kilowatt-hours per square meter per day for each month. The data were developed from the State University of New York's (SUNY) GOES satellite solar model. This model uses information on hourly satellite observed visible irradiance, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total of the normal or beam insolation falling on a tracking concentrator pointed directly at the sun. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 12% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Solar TILT Polygon Multiple Countries 10km SUNY 1998-2005 (tilt_suny_high_900913)

SRID 900913 of two available coordinate systems. These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 10 km by 10 km in size. The solar resource value is represented as kilowatt-hours per square meter per day for each month. The data were developed from the State University of New York's (SUNY) GOES satellite solar model. This model uses information on hourly satellite observed visible irradiance, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total of the normal or beam insolation falling on a tracking concentrator pointed directly at the sun. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 12% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Wind Power Class 50m Alaska NREL (us_wpc_2_8bit21)

GeoTIFF of Wind Power Class values for the state of Alaska. This map shows the annual average wind power estimates at a height of 50 meters. It is a combination of high resolution and low resolution datasets produced by NREL and other organizations. The data was screened to eliminate areas unlikely to be developed onshore due to land use or environmental issues. In many states, the wind resource on this map is visually enhanced to better show distribution on ridge crests and other features.

Wind Power Class 50m Hawaii NREL (us_wpc_2_8bit_HI)

GeoTIFF of Wind Power Class values for Hawaii. This map shows the annual average wind power estimates at a height of 50 meters. It is a combination of high resolution and low resolution datasets produced by NREL and other organizations. The data was screened to eliminate areas unlikely to be developed onshore due to land use or environmental issues. In many states, the wind resource on this map is visually enhanced to better show distribution on ridge crests and other features.

Wind Power Class 50m Continental US NREL (us_wpc_2_8bit_l48)

GeoTIFF of the Wind Power Class values for the continental United States. This map shows the annual average wind power estimates at a height of 50 meters. It is a combination of high resolution and low resolution datasets produced by NREL and other organizations. The data was screened to eliminate areas unlikely to be developed onshore due to land use or environmental issues. In many states, the wind resource on this map is visually enhanced to better show distribution on ridge crests and other features.

Wind Polygon Brazil 10km INPE 2008 (wind_inpe_high)

SRID 4326 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 10km x 10km (Purpose): The thematic map by code of colors permits quick viewing of all the Brazilian territory dataset. That map indicates, for the height of 50m, the annual average, in W/m2, of wind speed, power class, power density and Weibull k value (Supplemental Information): The information is organized into cells measuring 10 x 10km. The wind potential maps were calculated from simulations produced by the MesoMap(*) for 360 days, extracted of a period of 15 years of data. The days were chosen by means of random sampling at several heights, so that each month and season be considered in a representative way. MesoMap(*) for 360 days, extracted of a period of 15 years of data. The days were chosen by means of random sampling at several heights, so that each month and season be considered in a representative way. (*) MesoMap is an integrated group of atmospheric simulation models, geographical and meteorological databases, nets of computers and storage systems. The MesoMap has been checked by high quality anemometric measurements in a large wind regimens range.

Wind Polygon Brazil 10km INPE 2008 (wind_inpe_high_900913)

SRID 900913 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 10km x 10km (Purpose): The thematic map by code of colors permits quick viewing of all the Brazilian territory dataset. That map indicates, for the height of 50m, the annual average, in W/m2, of wind speed, power class, power density and Weibull k value (Supplemental Information): The information is organized into cells measuring 10 x 10km. The wind potential maps were calculated from simulations produced by the MesoMap(*) for 360 days, extracted of a period of 15 years of data. The days were chosen by means of random sampling at several heights, so that each month and season be considered in a representative way. MesoMap(*) for 360 days, extracted of a period of 15 years of data. The days were chosen by means of random sampling at several heights, so that each month and season be considered in a representative way. (*) MesoMap is an integrated group of atmospheric simulation models, geographical and meteorological databases, nets of computers and storage systems. The MesoMap has been checked by high quality anemometric measurements in a large wind regimens range.

Wind Polygon Brazil 40km CEPAL INPE 2009 (wind_inpe_mod)

SRID 4326 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 40km x 40km Source: CEPEL (Electric Energy Research Center/Federal University of Rio de Janeiro) - Brazil and INPE (National Institute for Space Research)

Wind Polygon Brazil 40km CEPAL INPE 2009 (wind_inpe_mod_900913)

SRID 900913 of two available coordinate systems. Annual average of the aeolic potential at 50m. Content: wind speed in m/s, power class (7 classes), power density in W/m2 and Weibull k value organized into cells with 40km x 40km Source: CEPEL (Electric Energy Research Center/Federal University of Rio de Janeiro) - Brazil and INPE (National Institute for Space Research)

Wind Polygon 1 Degree Global NASA 2005 (wind_nasa_low)

SRID 4326. Wind Speed At 50 m Above The Surface Of The Earth (m/s)NASA Surface meteorology and Solar Energy (SSE) Release 5 Data Set (Jan. 2005)10-year Monthly & Annual Average (July 1983 - June 1993) Source: U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE) Name or title (Title): Wind: monthly and annual average wind GIS data at one-degree resolution of the World from NASA/SSE

Wind Polygon 50m United States NREL (wind_nrel_high)

SRID 4326 of two available coordinate systems. Geographic shapefiles generated from the original raster data. The original raster data varied in resolution from 200-meter to 1000-meter cell sizes. The data provide an estimate of annual average wind resource for specific states or regions. The data are separated into two distinct groups: NREL produced, and AWS Truepower produced/NREL validated. The NREL-produced map data only applies to areas of low surface roughness (i.e. grassy plains), and excludes areas with slopes greater than 20%. For areas of high surface roughness (i.e. forests), the values shown may need to be reduced by one or more power classes. The TrueWind-produced resource estimates factor in surface roughness in their calculations, and do not exclude areas with slopes greater than 20%. These data were produced in cooperation with U.S. Department of Energy's Wind Powering America program, and have been validated by NREL and other wind energy meteorological consultants. Source: NREL

Wind Polygon 50m United States NREL (wind_nrel_high_900913)

SRID 900913 of two available coordinate systems. Geographic shapefiles generated from the original raster data. The original raster data varied in resolution from 200-meter to 1000-meter cell sizes. The data provide an estimate of annual average wind resource for specific states or regions. The data are separated into two distinct groups: NREL produced, and AWS Truepower produced/NREL validated. The NREL-produced map data only applies to areas of low surface roughness (i.e. grassy plains), and excludes areas with slopes greater than 20%. For areas of high surface roughness (i.e. forests), the values shown may need to be reduced by one or more power classes. The TrueWind-produced resource estimates factor in surface roughness in their calculations, and do not exclude areas with slopes greater than 20%. These data were produced in cooperation with U.S. Department of Energy's Wind Powering America program, and have been validated by NREL and other wind energy meteorological consultants. Source: NREL

Wind Polygon Multiple Countries 50m Riso-DTU 2008 (wind_riso_high)

SRID 4326 of two available coordinate systems. These data are results from the KAMM/WASP studies. Version 2 is an updated version of the earlier release and includes an adjustment to Weibull A and k to bring the Atlas values into better agreement with observations. The KAMM/WAsP methodology uses a set of wind classes to represent wind conditions for the mapped region. A mesoscale simulation for each wind class, using KAMM (Karlsruhe Mesoscale Model), is performed and statistics performed on the model output. The result is i. a wind resource map, a summary of the simulated wind climate, and ii. a wind atlas, a summary of the wind climate standardized to flat, uniform roughness terrain. (Purpose): The product is intended to be used to estimate the wind resource potential in the country including the the spatial variability. This map covers regions where long term measurements are not available. In a sense this is the point of the mapping exercise, but it also means that verification of results has not been as complete would be ideal. There is some concern that the results may underestimate the resource. However, new measurement data is coming and revisions to the map may be made if necessary as verification is carried out.

Wind Polygon Multiple Countries 50m Riso-DTU 2008 (wind_riso_high_900913)

SRID 900913 of two available coordinate systems. These data are results from the KAMM/WASP studies. Version 2 is an updated version of the earlier release and includes an adjustment to Weibull A and k to bring the Atlas values into better agreement with observations. The KAMM/WAsP methodology uses a set of wind classes to represent wind conditions for the mapped region. A mesoscale simulation for each wind class, using KAMM (Karlsruhe Mesoscale Model), is performed and statistics performed on the model output. The result is i. a wind resource map, a summary of the simulated wind climate, and ii. a wind atlas, a summary of the wind climate standardized to flat, uniform roughness terrain. (Purpose): The product is intended to be used to estimate the wind resource potential in the country including the the spatial variability. This map covers regions where long term measurements are not available. In a sense this is the point of the mapping exercise, but it also means that verification of results has not been as complete would be ideal. There is some concern that the results may underestimate the resource. However, new measurement data is coming and revisions to the map may be made if necessary as verification is carried out.

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