The Wind Integration National Dataset (WIND) Toolkit, developed by the National Renewable Energy Laboratory (NREL), provides modeled wind speeds at multiple elevations. Instantaneous wind measurements were analyzed from more than 126,000 sites in the continental United States for the years 2007–2013. The model results were mapped on a 2-km grid. A subset of the contiguous United States data for 2012 is shown here. Offshore data is shown to 50 nautical miles.Time Extent: Annual 2012Units: m/sCell Size: 2 kmSource Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection:  WGS 1984 Web MercatorExtent: Contiguous United StatesSource: NREL Wind Integration National Dataset v1.1

WIND is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.

Accessing Elevation InformationEach of the 9 elevation slices can be accessed, visualized, and analyzed. In ArcGIS Pro, go to the Multidimensional Ribbon and use the Elevation pull-down menu. In ArcGIS Online, it is best to use Web Map Viewer Classic where the elevation slider will automatically appear on the righthand side. The elevation slider will be available in the new Map Viewer in an upcoming release.

What can you do with this layer?

This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides the pixel’s wind speed value.

This analytical imagery tile layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and proposed wind turbine locations can be used to Sample the layer at multiple elevation to determine the optimal hub height. Source data can be accessed on Amazon Web ServicesUsers of the WIND Toolkit should use the following citations:Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.

GIS data for Bhutan's Wind Power Density at 50m Above Ground Level. NREL developed estimates of Bhutans wind resources at a spatial resolution of 1 km^2 using NREL?s Wind Resource Assessment and Mapping System (WRAMS). Wind turbine output at a given site can be predicted using wind speed data and the turbine's power curve, which describes the turbines operating power at different wind speeds. Using data found from this analysis, estimates can be made for the best potential locations for wind energy throughout Bhutan.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric 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. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

description: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the United States of America lower 48 states. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric 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. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in kilowatt hours per meter squared per day. OtherCitation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM. Marion, William and Stephen Wilcox, 1994: "Solar Radiation Data Manual for Flat-plate and Concentrating Collectors". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401. ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.; abstract: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the United States of America lower 48 states. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric 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. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in kilowatt hours per meter squared per day. OtherCitation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM. Marion, William and Stephen Wilcox, 1994: "Solar Radiation Data Manual for Flat-plate and Concentrating Collectors". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401. ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

SMU Geothermal Lab developed a methodology to estimate shallow (1 km to 4 km) Enhanced Geothermal Systems (EGS) resource potential using an approach that utilizes recent geology and geophysical research along with new well data to improve the thermal conductivity model, mitigate impacts from groundwater flow in the thermal model, and examine radioactivity data variations. By incorporating the results of the most recent projects with the SMU shallow methodology, we developed a more accurate, updated resource estimate for the Snake River Plain (SRP). The resulting maps and resource estimates can be used by the National Renewable Energy Lab (NREL), Bureau of Land Management (BLM), and the public to determine how best to move forward with future project development.

In 2005, the National Renewable Energy Laboratory (NREL) completed an assessment of the potential for solar and wind energy development on National Forest System (NFS) public lands managed by the US Department of Agriculture, U.S. Forest Service (USFS). This report provides an update of the analysis in the NREL report, and extends the analysis with additional siting factors for solar and wind energy. It also expands the scope to biomass and geothermal energy resources. Hydropower is acknowledged as another major renewable energy source on NFS lands; however, it was not analyzed in this project primarily because of the substantially different analysis that would be needed to identify suitable locations.Details about each renewable energy production technology included in the study are provided following the report introduction, including how each resource is converted to electrical power, and examples of existing power plants. The analysis approach was to use current and available Geographic Information System (GIS) data to map the distribution of the subject renewable energy resources, major siting factors, and NFS lands. For each major category of renewable energy power production, a set of siting factors were determined, including minimum levels for the renewable energy resources, and details for each of the other siting factors. Phase 1 of the analysis focused on replicating and updating the 2005 NREL analysis, and Phase 2 introduced additional siting factors and energy resources. Source data were converted to a cell?based format that helped create composite maps of locations meeting all the siting criteria. Acreages and potential power production levels for NFS units were tabulated and are presented throughout this report and the accompanying files.This photovoltaic solar power raster dataset incorporates Phase 2 siting factors (see pdf report, Analysis of Renewable Energy Potential on U.S. National Forest Lands for more details).

Annual average wind resource potential for the state of Colorado at a 50 meter height. Includes the wind power class, an indicator of likely resource strength. Generated for Colorado by the National Renewable Energy Laboratory (NREL). Navigate to NREL source for most current information.

The annual average direct normal solar resources by 12-Digit Hydrologic Unit (HUC) was estimated from maps produced by the National Renewable Energy Laboratory for the U.S. Department of Energy (February 2009). The original data was from 10km, satellite modeled dataset (SUNY/NREL, 2007) representing data from 1998-2005. The 10km data was converted to 30m grid cells, and then zonal statistics were estimated for a final value of average kWh/m2/day for each 12-digit HUC. For more information about the original dataset please refer to the National Renewable Energy Laboratory (NREL) website at www.nrel.gov/gis/data_solar.html. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

description: Annual average wind resource potential of the northwestern United States at a 50 meter height. This data set has been validated by NREL and wind energy meteorological consultants. Note: This data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 400 m resolution, in a UTM zone 11, datum WGS 84 projection system. The resource information for the Confederated Tribes of the Umatilla reservation is not being made available as part of this data set at their request. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.; abstract: Annual average wind resource potential of the northwestern United States at a 50 meter height. This data set has been validated by NREL and wind energy meteorological consultants. Note: This data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 400 m resolution, in a UTM zone 11, datum WGS 84 projection system. The resource information for the Confederated Tribes of the Umatilla reservation is not being made available as part of this data set at their request. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

description: Annual average wind resource potential for the state of New Mexico at a 50 meter height. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.; abstract: Annual average wind resource potential for the state of New Mexico at a 50 meter height. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for the city of Orlando in Florida. The rooftop solar technical potential information is based on data from two different datasets. The first dataset is LiDAR data for the city of Orlando obtained from the Orlando Utilities Commission (OUC) (Koebrich et al. 2021). The second dataset is a national parcel dataset (HIFLD 2020) which contains descriptive data and geometries for parcels in the U.S. Parcel scale data from both these datasets have been processed and aggregated to block scale to produce these html maps. The first html map (block scale developable roof area for Orlando) contains the developable roof area for solar. The second html map (block scale rooftop solar technical potential for Orlando) contains the rooftop solar technical potential in units of kilowatts as well as additional information on the most common building use type and the most common building occupancy type for the block. These html maps are provided to demonstrate the proof-of-concept analysis conducted for Orlando.

Overview: Daily maps for global daylight length, calculated for the year 2022.

Processing steps: For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes.

File naming scheme (DDD = day within year) (min is abbreviation for minute): daylight_min_2022_DDD.tif

Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent: north: 90 south: -90 west: -180 east: 180

Spatial resolution: 30 arc seconds (approx. 1000 m)

Temporal resolution: Daily

Pixel values: unit: minutes

Software used: GDAL 3.2.2 and GRASS GIS 8.2.0

License: CC-BY-SA 4.0

Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Reference: National Renewable Energy Laboratory (NREL): SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html)

GIS data for India's direct normal irradiance (DNI) and global horizontal irradiance.

Provides 10-kilometer (km) solar resource maps and data for India. The 10-km hourly solar resource data were developed using weather satellite (METEOSAT) measurements incorporated into a site-time specific solar modeling approach developed at the U.S. State University of New York at Albany. The data is made publicly available in geographic information system (GIS) format (shape files etc).

The new maps and data were released in June 2013. The new data expands the time period of analysis from 2002-2007 to 2002-2011 and incorporates enhanced aerosols information to improve direct normal irradiance (DNI).

These products were developed by the U.S. National Renewable Energy Laboratory (NREL) in cooperation with India's Ministry of New and Renewable Energy, through funding from the U.S. Department of Energy and U.S. Department of State.

This dataset identifies counties and municipalities that had a solar energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where solar energy bans or moratoriums exist across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

A shapefile of annual average wind resource potential for Puerto Rico and the Virgin Islands at a 50 meter height. This data set has been validated by NREL and wind energy meteorological consultants. Note: This data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a UTM zone 12, datum WGS 84 projection system. The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

description: Annual average wind resource potential for the state of Nevada, United States at a 50 meter height. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.; abstract: Annual average wind resource potential for the state of Nevada, United States at a 50 meter height. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

Average daily total solar global horizontal irradiance average from 1998 to 2014 from the National Renewable Energy Laboratory (NREL).

This EnviroAtlas dataset shows the annual average potential wind energy resource in kilowatt hours per square meter per day for each 12-digit Hydrologic Unit (HUC). It was produced using data from the National Renewable Energy Laboratory (NREL). These estimates represent wind resources at a 10 meter height above surface. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Annual average wind resource potential for the state of Tennessee at a 50 meter height.

This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a UTM zone 12, datum WGS 84 projection system.

The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.