This map displays the wind forecast over the next 72 hours across the contiguous United States, in 3 hour increments, including wind direction, wind gust, and sustained wind speed.Zoom in on the Map to refine the detail for a desired area. The Wind Gust is the maximum 3-second wind speed (in mph) forecast to occur within a 2-minute interval within a 3 hour period at a height of 10 meters Above Ground Level (AGL). The Wind Speed is the expected sustained wind speed (in mph) for the indicated 3 hour period at a height of 10 meters AGL. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Wind Speed Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wspd.binWind Gust Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wgust.binWind Direction Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wdir.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.Alternate SymbologyFeature Layer item that uses Vector Marker Symbols to render point arrows, easily altered by user. The color palette uses the Beaufort Scale for Wind Speed. https://www.arcgis.com/home/item.html?id=45cd2d4f5b9a4f299182c518ffa15977 This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2023.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

For further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.

This nowCOASTâ„¢ time-offsets map service provides maps depicting the NWS surface wind speed forecasts from the National Digital Forecast Database (NDFD) at 6-hr increments out to 3 days (NDFD has forecasts out to 7 days which are available via the nowCOASTâ„¢ time enabled map service for NDFD elements). Each forecast is valid for the specified forecast projection hour with respect to the latest update cycle time. The forecast is valid at 10 m (33 feet) above ground level. The wind speeds are in units of knots (1 knot = 1.15 miles per hour). The wind speed forecast is indicated on the map by different colors for 5-knot increments up to 60 knots (69 mph) and then at 10-knot increments up to 100 knots (115 mph). The forecasts are updated in the nowCOASTâ„¢ map service four times per day. For more detailed information about layer update frequency and timing, please reference the nowCOASTâ„¢ Dataset Update Schedule.

An archive of wind fields retrieved from satellite Synthetic Aperture Radar (SAR) observations over the ocean by the Technical University of Denmark (DTU, https://ror.org/04qtj9h94). The wind fields are instantaneous ocean backscatter measurements converted to ocean winds at 10m above the sea surface, through the implementation of dedicated retrieval algorithms and input wind directions from numerical models. The archive is updated daily and users can browse and download the wind fields though a web interface. The maps are available in .nc and .png format. The archive of wind fields is generated with the SAR Ocean Products System (SAROPS) developed by the Johns Hopkins University, Applied Physics Laboratory (JHU/APL, https://ror.org/029pp9z10) and the US National Atmospheric and Oceanographic Administration (NOAA, https://ror.org/02z5nhe81). The system uses input data from the following sources:

Envisat [2002-12] and Copernicus Sentinel-1 [2014-present] satellite SAR data provided by the European Space Agency (ESA, https://ror.org/03wd9za21).

Wind directions [2002-10] from the Climate Forecast System Reanalysis (CFSR) by the National Centers for Environmental Prediction (NCEP, https://ror.org/00ndyev54).

Wind directions [2011-present] from the Global Forecast System (GFS) by the National Centers for Environmental Prediction (NCEP, https://ror.org/00ndyev54).

Land surface topography data from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG).1

Sea ice data [2002-present] from IMS Daily Northern Hemisphere Snow and Ice Analysis.2

1 Wessel, P., and W. H. F. Smith, 1996. A Global Self-consistent, Hierarchical, High-resolution Shoreline Database, J. Geophys. Res., 101, 8741-8743. https://doi.org/10.1029/96JB00104 2 U.S. National Ice Center. 2008, updated daily. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. [2002-present]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. https://doi.org/10.7265/N52R3PMC.

GIS data for offshore wind speed (meters/second) at a 90 meter height above surface level. The data is specified to Exclusive Economic Zones (EEZ). The wind resource is based on NOAA Blended Sea Winds and monthly wind speed at 30km resolution from 1987-2005, using a 0.11 wind shear to extrapolate 10m - 90m. Annual average greater than or equal to 10 months of data, no nulls.

The NOAA Blended Sea Winds dataset contains ocean surface vector winds and wind stresses gridded at 0.25 degrees. Multiple time resolutions are available: 6-hour, daily, and monthly. Wind speeds were generated from satellite observations; directions, from a combination of National Centers for Environmental Prediction (NCEP) Reanalysis and European Center for Medium-Range Weather Forecasts (ECMWF) data assimilation products.

Hub height is an important determinant of wind resource at a given location. Due to drag close to ground-level, wind speeds fall at lower altitudes. Over rough terrain, that drop can be precipitous, but there is substantial drag even over relatively smooth ocean surfaces. Wind speeds in the Blended Sea Winds database are at 10 m above ground level. To extrapolate them to 90m heights, a power-law wind-shear adjustment using a shear exponent of 0.11 was applied. The exponent value was chosen based on the guidance of Schwartz et al. (2010), who support its use for U.S. marine areas. The coarseness of the escalation assumption is regretful but necessary given this dataset.

There were some missing months in the dataset, especially at polar latitudes. For cells with at least 10 months of data, the 10-month average was considered as the annual average; for cells with fewer than 10 months of data, no resource was given. As those grid cells tended to be at extreme northern latitudes, and the missing months were generally in winter, it is assumed that the gaps are to be ice-caused and likely those sites are too icy for economic wind development.

For more up to date data please visit the "Wind Resource Database" link below.

The UK mean wind data describes the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data is collected by observation stations across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to present.

The Canadian Wind Turbine Database contains the geographic location and key technology details for wind turbines installed in Canada. This dataset was jointly compiled by researchers at CanmetENERGY-Ottawa and by the Centre for Applied Business Research in Energy and the Environment at the University of Alberta, under contract from Natural Resources Canada. Additional contributions were made by the Department of Civil & Mineral Engineering at the University of Toronto. Note that total project capacity was sourced from publicly available information, and may not match the sum of individual turbine rated capacity due to de-rating and other factors. The turbine numbering scheme adopted for this database is not intended to match the developer’s asset numbering. This database will be updated in the future. If you are aware of any errors, and would like to provide additional information, or for general inquiries, please use the contact email address listed on this page.

National Digital Forecast Database (NDFD) MetadataThe National Digital Forecast Database (NDFD) Web Services provide a set of gridded weather forecasts for various sensible weather elements in near real-time. These forecasts are generated by a collaboration between the National Weather Service (NWS) field offices and the National Centers for Environmental Prediction (NCEP). The NDFD Web Services offer a seamless, digital mosaic of weather forecasts that can be accessed by users to obtain up-to-date information on a variety of weather conditions.The NDFD's forecasts are gridded, meaning they cover large geographic areas with weather data at specific intervals, providing high-resolution, geographically distributed forecasts. These forecasts can include temperature, precipitation, wind speed and direction, cloud cover, and other meteorological parameters.These web services are hosted by the Office of Dissemination’s CloudGIS team, which ensures the forecasts are readily accessible and deliverable over the internet. Users, including meteorologists, developers, and anyone interested in weather data, can query these web services for up-to-date forecasts in a digital format, enabling integration into applications, websites, and other platforms.NDFD’s Web Services Descriptions:12-Hour Probability of Precipitation Web Service's data layer is the likelihood, expressed as a percent, of a measurable precipitation event (1/100th of an inch or more) at a grid point during the 12-hour valid period. The 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web Service: contains data that is the perceived temperature derived from either a combination of temperature and wind (Wind Chill) or temperature and humidity (Heat Index) for the indicated hour. When the temperature at a particular grid point falls to 50 F or less, wind chill will be used for that point for the Apparent Temperature. When the temperature at a grid point rises above 80 F, the heat index will be used for Apparent Temperature. Between 51 and 80 F, the Apparent Temperature will be the ambient air temperature.Dew Point Temperature Web Service's data is the expected dew point temperature for the indicated hour. Dew point temperature is a measure of atmospheric moisture. It is the temperature to which air must be cooled in order to reach saturation (assuming air pressure and moisture content are constant).Maximum Temperature Web Service's data is the daytime maximum temperature observed from 7 AM to 7PM LST.Minimum Temperature Web Service's data is predicted minimum temperature for a specific location at a given time, allowing users to visualize the lowest expected temperatures across a geographical area.Precipitation Amount Web Service's data is the expected quantity of liquid precipitation accumulated over a six-hourly period. A quantitative precipitation forecast (QPF) will be specified when a measurable (1/100th of an inch or more) precipitation type is forecast for any hour during a QPF valid period. NDFD valid periods for QPF are 6 hours long beginning and ending at 0000, 0600, 1200 and 1800 UTC. QPF includes the liquid equivalent amount for snow and ice.Relative Humidity Web Service's data is a ratio, expressed as a percent, of the amount of atmospheric moisture present relative to the amount that would be present if the air were saturated. Since the latter amount is dependent on temperature, relative humidity is a function of both moisture content and temperature.Sky Cover Web Service’s data is the predicted percentage of the sky that will be covered by opaque clouds at a given time, provided by the National Digital Forecast Database (NDFD). It is a forecast of how much of the sky will be obscured by clouds, expressed as a percentage value.Snow Amount Web Service's data is the expected total accumulation of new snow during a 6-hour period. A snow accumulation grid will be specified whenever a measurable snowfall is forecast for any hour during a valid period. Valid periods for the NDFD begin and end at 0600, 1200, 1800, and 0000 UTC.Temperature Web Service: contains data that is the expected temperature in degrees Fahrenheit valid for the indicated hour.Wave Height Web Service's data is the average height (from trough to crest) of the one-third highest waves valid for the top of the designated hour. Wave Height is a combination of wind waves and swell.Wind Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass.Wind Gust Web Service's data is the maximum 3-second wind speed forecast to occur within a 2-minute interval at a height of 10 meters. Wind gust forecasts are valid at the top of the indicated hour.Wind Speed Web Service's data is the expected sustained 10-meter sustained wind speed for the indicated hour.Wind Speed and Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass. Wind Speed is the expected sustained 10-meter sustained wind speed for the indicated hour. Wind barbs (shown below) are used to denote wind speed and direction.Update Frequency: The data in these service updates hourly. (Click here to see specific Valid Times for update Frequency)Link to graphical web page: https://digital.weather.govLink to data download (grib2): https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:These web services are time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.These particular services can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.Valid Time Table:ServiceValid Time12-Hour Probability of Precipitation Web ServiceThe 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Dew Point Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Maximum Temperature Web ServiceDisplay 0z every dayMinimum Temperature Web ServiceDisplay at 12z every dayPrecipitation Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Relative Humidity Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Sky Cover Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Snow Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wave Height Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Gust Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind SpeedCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Speed and Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)

This dataset contains global onshore and offshore wind supply curves based on a resource assessment performed at the National Renewable Energy Laboratory (NREL) based on the National Center for Atmospheric Research's (NCAR) Climate Four Dimensional Data Assimilation (CFDDA) mesoscale climate database. This overview is intended to provide a brief description of the origin of the tables in this workbook, not to fully explain the assumptions and calculations involved. The paper linked below includes full detail of sources and assumptions.

The supply curves are defined by country and resource quality. Onshore supply curves are further differentiated by distance to nearest large load or power plant, and offshore by distance to shore and water depth.

The CFDDA database contains hourly wind velocity vectors at a 40km grid, at multiple heights above ground level. For each grid cell, we create hourly wind speed distributions at 90m hub heights, and we compute gross capacity factor through convolution with a representative power curve. Output is derated for outages and wake losses to obtain net capacity factor. Onshore, we assumed a composite IEC Class II turbine; offshore, an IEC Class I turbine. We assumed a wind turbine density of 5 MW/km.

Land and sea area are characterized by country (or country-like object, e.g, Alaska), land use/land cover, elevation, and protection status. Protected, urban, and high-elevation areas are fully excluded, and certain land cover types are fractionally excluded. Offshore, area within 5 nautical miles of or farther than 100 nautical miles from shore are excluded, as are protected marine areas. Marine areas are assigned to country based on exclusive economic zones; unassigned or disputed areas are excluded.

As alluded to previously, in this workbook, "United States of America" refers only to the continental U.S. Alaska and Hawaii are counted separately because of their remoteness. Unassigned "countries" comprise relatively remote, unpopulated areas (Alaska, Greenland, remote islands); and disputed marine areas. We recommend that their resource remain unassigned rather than grouped into larger IAM regions.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2018), with the data processed through September 30, 2018. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2018) and processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) is computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation, and disaggregated to hourly PET by using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as "backup". Temporal variations in the statistical properties of the data resulting from changes in measurement and data ...

This map displays the wind forecast over the next 72 hours across the contiguous United States, in 3 hour increments, including wind direction, wind gust, and sustained wind speed.

Zoom in on the Map to refine the detail for a desired area. The Wind Gust is the maximum 3-second wind speed (in mph) forecast to occur within a 2-minute interval within a 3 hour period at a height of 10 meters Above Ground Level (AGL). The Wind Speed is the expected sustained wind speed (in mph) for the indicated 3 hour period at a height of 10 meters AGL. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.

Where is the data coming from?

The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).

Wind Speed Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wspd.bin

Wind Gust Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wgust.bin

Wind Direction Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wdir.bin

Where can I find other NDFD data?

The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.

What can you do with this layer?

This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.

Alternate Symbology Feature Layer item that uses Vector Marker Symbols to render point arrows, easily altered by user. The color palette uses the Beaufort Scale for Wind Speed. https://www.arcgis.com/home/item.html?id=45cd2d4f5b9a4f299182c518ffa15977 This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page.

Maps with wind speed, wind rose and wind power density potential in Poland. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

[Metadata] Description: Wind Energy Resource Data collected using the MesoMap system. Wind speed in the state of Hawaii for the height of 30 meters above ground.

Herewith we present the dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag.

The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N.

Presented data were gathered between years 2009-2015;

Hardware summary: 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away).

Database summary: approx. 180 000 of 10 min data samples of full data recovery. Wind speed and direction, temperature, pressure & relative humidity (from a nearby meteostation).

Data description: Two data files of different formats are available: a ‘*.txt’ comma-separated values file and a native MATLAB ‘*.mat’ file. Both contain the same data, starting with the first column: timestamp, wind speed (m/s, columns WS1-WS12) for 6 anemometers pairs, wind direction (360 deg, columns WD1-WD12) for 6 anemometers pairs, temperature at 0.2 m (AT0), temperatures at levels of wind measurement (deg C, AT1-AT6), data from nearby meteostation Sula, pressure (hPa, PressureSula), relative humidity (%, RelHumSula), temperature (deg C, TempSula), wind direction (360 deg, WDSula) and wind speed (m/s, WSSula). Columns have headers describing the data (first row).

Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf.

Additional information and analysis can be found in listed below works, using data from Frøya site, or nearby sites:

Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. https://wes.copernicus.org/articles/5/391/2020/

IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0

Domagalski, P., Bardal, L. M., & Satran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3

Mathias Møller , Piotr Domagalski and Lars Roar Sætran, Characteristics of abnormal vertical wind profiles at a coastal site, Journal of Physics: Conference Series, IOPscience, under review (Feb 2019), DeepWind2019 conference poster available at: https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf

Bardal, L. M., Onstad, A. E., Sætran, L. R., & Lund, J. A. (2018). Evaluation of methods for estimating atmospheric stability at two coastal sites. Wind Engineering, 0309524X18780378, https://doi.org/10.1177/0309524X18780378

Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf

Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207

The text file "Wind speed.txt" contains hourly wind speed data in miles per hour and associated data source flags from January 1, 1948, to September 30, 2019. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN18.WDM (Bera, 2019) and renamed as ARGN19.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The hourly wind speed data from the Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, and the National Weather Service station at O'Hare International Airport were used as "backup". Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2019) provided the data collected by National Weather Service from the station at O'Hare International Airport. Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://gonzalo.er.anl.gov/ANLMET/. Bera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, ​https://doi.org/10.5066/P9H8P0F7. Midwestern Regional Climate Center, 2019, Meteorological data, accessed on November 6, 2019, at https://mrcc.illinois.edu/CLIMATE/. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2019. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.

This map displays the forecasted wind speeds over the next 72 hours across the contiguous United States. Wind Speed is the expected 10-meter Above Ground Level (AGL) sustained wind speed (in knots) for the indicated hour. Wind speed forecasts are valid at the top of the indicated hour. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces forecast data of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wspd.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The files contain 10 minutes database of wind speed and meteorological data.

The database is used in the paper "Long-term estimation of wind power by probabilistic forecast using genetic programming" published in the Journal Energies, April 2020.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value is assigned a corresponding data source flag that indicates the source of the value and its transformations. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2015) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2017) station at Chicago O'Hare International Airport is used as "backup" for the dewpoint temperature and wind speed data. Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2017, Meteorological data, accessed on October 25, 2017, at URL http://gonzalo.er.anl.gov/ANLMET/. Bera, M., and Over, T. M., 2017, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7SJ1HS5. Midwestern Regional Climate Center, 2017, Meteorological data, accessed on December 5, 2017, at URL http://mrcc.isws.illinois.edu/CLIMATE/welcome.jsp. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2015, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on December 5, 2017, at http://dx.doi.org/10.13012/J8MW2F2Q.

This dataset consists of annual CSV files containing multiple sources of modeled, hourly wind speeds and generation. For complete information about this dataset, including validation of modeled generation versus recorded generation, please see the Scientific Data article: Millstein, D., Jeong, S., Ancell, A., & Wiser, R. (2023). A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models. Scientific Data, 10(1), 883. https://doi.org/10.1038/s41597-023-02804-w

This map contains the generalize annual mean wind speed [m/s] at 100 m a.g.l. over the flat terrain and uniform roughness of 0.03 m.

The Wind Atlas for South Africa (WASA) Project is an initiative of the South African Dept of Energy (DoE) with the South African National Energy Development Institute (SANEDI) executing, managing WASA and contracting the Implementation Partners: The South African Council for Scientific and Industrial Research (CSIR), University of Cape Town (Climate Systems Analysis Group) (UCT CSAG), South African Weather Services (SAWS) and Department of Wind Energy, Technical University of Denmark (DTU Wind Energy). The main objective of WASA through capacity development and research cooperation is to develop and employ numerical (modelled) wind atlas methods and to develop capacity to enable long term planning of large-scale exploitation of wind power in South Africa, including dedicated wind resource assessment and siting tools for planning purposes, i.e. a Verified with physical wind measurements Numerical (modelled) Wind Atlas and database for South Africa. More information can be found following the link: http://www.wasaproject.info/.