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!

Context

High precision and reliable wind speed forecasting is a challenge for meteorologists. Severe wind due to convective storms, causes considerable damages (large scale forest damage, outage, buildings/houses damage, etc.). Convective events such as thunderstorms, tornadoes as well as large hail, strong winds, are natural hazards that have the potential to disrupt daily life, especially over complex terrain favoring the initiation of convection. Even ordinary convective events produce severe winds which causes fatal and costly damages. Therefore, wind speed prediction is an important task to get advanced severe weather warning. This dataset contains the responses of a weather sensor that collected different weather variables such as temperatures and precipitation.

Content

The dataset contains 6574 instances of daily averaged responses from an array of 5 weather variables sensors embedded in a meteorological station. The device was located on the field in a significantly empty area, at 21M. Data were recorded from January 1961 to December 1978 (17 years). Ground Truth daily averaged precipitations, maximum and minimum temperatures, and grass minimum temperature were provided.

Attribute Information

1. DATE (YYYY-MM-DD)
2. WIND: Average wind speed [knots]
3. IND: First indicator value
4. RAIN: Precipitation Amount (mm)
5. IND.1: Second indicator value
6. T.MAX: Maximum Temperature (°C)
7. IND.2: Third indicator value
8. T.MIN: Minimum Temperature (°C)
9. T.MIN.G: 09utc Grass Minimum Temperature (°C)

Citation Request

If you want to cite this data:

fedesoriano. (April 2022). Wind Speed Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/fedesoriano/wind-speed-prediction-dataset

The Global Wind Atlas version 3 data-sets contain microscale wind information at approximately 250m grid point spacing.The data is created by first dynamically down-scaling ERA5 reanalysis data from 2008-2017 to 3km resolution using the WRF mesoscale model.The WRF results are then generalized using DTU's generalization methodology, and then down-scaled using the WAsP model to the final 250m resolution.The data in this directory consist of the entire global tiff at the full 0.0025 degree resolution on the WGS84 map projection. These data also include four sets of overview pyramids to improve the viewing of the data at low resolution.Most of the data are named as follows: gwa_{variable}_{height}.tif, where variable is one of* wind-speed - The mean wind speed at the location for the 10 year period* power-density - The mean power density of the wind, which is related to the cube of the wind speed, and can provide additional information about the strength of the wind not found in the mean wind speed alone.* combined-Weibull-A and combined-Weibull-k - These are the all sector combined Weibull distribution parameters for the wind speed. They can be used to get an estimate of the wind speed and power density at a site. However, caution should be applied when using these in areas with wind speeds that come from multiple directions as the shapes of those individual distributions may be quite different than this combined distribution.* air-density - The air density is found by interpolating the air density from the CFSR reanalysis to the elevation used in the global wind atlas following the approach described in WAsP 12.* RIX - The RIX (Ruggedness IndeX) is a measure of how complex the terrain is. It provides the percent of the area within 10 km of the position that have slopes over 30-degrees. A RIX value greater than 5 suggests that you should use caution when interpreting the results.The files which do not follow the naming convention above are the capacity-factor layers. The capacity factor layers were calculated for 3 distinct wind turbines, with 100m hub height and rotor diameters of 112, 126, and 136m, which fall into three IEC Classes (IEC1, IEC2, and IEC3). Capacity factors can be used to calculate a preliminary estimate of the energy yield of a wind turbine (in the MW range), when placed at a location. This can be done by multiplying the rated power of the wind turbine by the capacity factor for the location (and the number of hours in a year): AEP = Prated*CF*8760 hr/year, where AEP is annual energy production, Prated is rated power, and CF is capacity factor.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The purpose of creating this file was to use MesoMap to create high-resolution wind maps of the state and to provide wind resource data in a format enabling the assessment of potential wind development sites in a GIS. By combining a sophisticated numerical weather model capable of simulating large-scale wind patterns with a microscale wind flow model responsive to local terrain and surface conditions - they enable the mapping of wind resources with much greater accuracy than has been possible in the past. In addition - they do not require surface wind data to make accurate predictions. While on-site measurements will be required to confirm the predicted wind resource at any particular location - mesoscale-microscale modeling can greatly reduce the time and cost required to identify and evaluate potential wind project sites. This map was created by AWS Truepower - LLC using the MesoMap system and historical weather data. Although it is believed to represent an accurate overall picture of the wind energy resource - estimates at any location should be confirmed by measurement. Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_OffshoreWindEnergyPlanning/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

This dataset consists of high resolution sea surface winds data produced from Synthetic Aperture Radar (SAR) on board the RADARSAT-2 satellite. The basic archive file is a netCDF-4 file containing SAR wind, a land mask, and time and earth location information. Maps of the SAR wind data in GeoTIFF format are also included. The product covers the geographic extent of the SAR image frame from which it was derived. These SAR-derived high resolution wind products are calculated from high resolution SAR images of normalized radar cross section (NRCS) of the Earth's surface. Backscattered microwave radar returns from the ocean surface are strongly dependent on wind speed and direction. When no wind is present, the surface of the water is smooth, almost glass-like. Radar energy will largely be reflected away and the radar cross section will be low. As the wind begins to blow, the surface roughens and surface waves begin to develop. As the wind continues to blow more strongly, the amplitude of the wave increases, thus, roughening the surface more. As the surface roughness increases, more energy is backscattered and NRCS increases. Moreover, careful examination of the wind-generated waves reveals that these surface wave crests are generally aligned perpendicular to the prevailing wind direction, suggesting a dependence of backscatter on the relative direction between the incident radar energy and the wind direction.

The purpose of creating this file was to use MesoMap to create high-resolution wind maps of the state and to provide wind resource data in a format enabling the assessment of potential wind development sites in a GIS. By combining a sophisticated numerical weather model capable of simulating large-scale wind patterns with a microscale wind flow model responsive to local terrain and surface conditions, they enable the mapping of wind resources with much greater accuracy than has been possible in the past. In addition, they do not require surface wind data to make accurate predictions. While on-site measurements will be required to confirm the predicted wind resource at any particular location, mesoscale-microscale modeling can greatly reduce the time and cost required to identify and evaluate potential wind project sites. This map was created by AWS Truepower, LLC using the MesoMap system and historical weather data. Although it is believed to represent an accurate overall picture of the wind energy resource, estimates at any location should be confirmed by measurement.

This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Context and Inspiration: The dataset was conceived out of the necessity to understand the dynamic relationship between meteorological variables and their impact on wind power generation. By collecting data directly from the field and the wind turbine installations, we aim to provide a comprehensive and authentic dataset that can be instrumental for industry-specific research, operational optimization, and academic purposes.

Data Collection: Data was meticulously gathered using state-of-the-art equipment installed at the site. The meteorological instruments measured temperature, humidity, dew point, and wind characteristics at different heights, while power generation data was recorded from the wind turbines' output. This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Potential Uses: This dataset is ideal for industry experts, researchers, and data scientists exploring renewable energy, especially wind power. It can aid in developing predictive models for power generation, studying environmental impacts on renewable energy sources, and enhancing operational efficiency in wind farms.

Eight days of location-specific quarter-hourly wind speed and wind direction data for the research paper "Multi-resolution spatio-temporal prediction with application to wind power generation"

Manitoba Agriculture operates a network of over 100 weather stations across Manitoba's agricultural region which provide hourly updated air temperature, relative humidity, precipitation, wind speed and direction, soil temperature and soil moisture. The WeatherStations feature class displays the location of the weather stations and its table contains a link to current weather for each station.Fields included:

Weather Stations (StnName)

Weather station name

Latitude (LatDD)

Latitude in decimal degrees

Longitude (LongDD)

Longitude in decimal degrees

Elevation (m) (Elevation)

Elevation in metres above sea level

AgRegion

Local geographic region for agriculture management in Manitoba

More information (URL)

Website link to information sheet on this location

These data represent the average monthly wind speed and direction at the surface of the ocean. Source data includes values from January 1, 1979, to December 31, 2010, at hourly temporal resolution, with a spatial resolution of 0.313 degrees latitude x 0.312 degrees longitude. Values for wind speed are in meters per second and wind direction in degrees from True North.

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States.

The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page.

For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below.

No filters have been applied to the raw WRF output.

Continuous dataset collection since 2014 through Urban Observatory (UO) sensors. The data covers the geographical area of the North East of England centred on Newcastle upon Tyne (for geographical extent and location of sensors see http://uoweb1.ncl.ac.uk). Data is collected from a variety of sensor platforms with different performance metrics, sampling regimes and sensitivity levels. Information on individual sensors should be consulted before use at http://uoweb1.ncl.ac.uk. Data can be downloaded or accessed via a REST API at http://uoweb1.ncl.ac.uk. Weather metrics include: Rain Int, Solar Radiation, Max Wind Speed, Rain Acc, Humidity, Pressure, Temperature, Wind Direction, Daily Accumulation Rainfall, Visibility, Wind Speed, Wind Gust, Rainfall

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period.

The Wind Atlas of Switzerland describes the wind conditions at five different heights above the ground: 50, 75, 100, 125 and 150 metres. The data are based on a nation-wide modelling system with a horizontal grid width of 100 metres. The modelled average annual wind speed is depicted in the atlas at each grid point. The classification of wind speeds into categories can be approximated with the aid of Weibull parameters A (scale parameter) and k (shape parameter). It is not possible to directly derive the average wind speed from the Weibull parameters because the result is only an approximation to wind distribution and this cannot be adequately reflected for each location. The wind rose shows the relative frequency of the modelled wind directions. The averaged wind speeds and corresponding Weibull parameters are visible for each sector. The calculation of wind speeds and directions is based on long-term measurements that have been incorporated into the models. Because the measurement points are not available everywhere throughout the country at a suitable density, and inaccuracies can occur in the modelling of wind flows in complex terrain, the results are subject to uncertainties. These range from +/- 0.5 metres per second in the Jura range, +/- 0.7 metres per second in the central plain and +/- 0.5 metres per second in the pre-Alps, to +/- 1.3 metres per second in the Alps. For maps at heights of more than 100 metres above the ground, significantly fewer measurements are available for modelling purposes, and this leads to increased uncertainties in the results. The data have to be regarded as rough estimates of the wind conditions. To assess the wind conditions at a specific location, measurement on site is therefore essential.

EOOffshore is a Sustainable Energy Authority of Ireland (SEAI) funded project, which commenced in June 2020 in the School of Physics in University College Dublin (UCD). It presents a case study that demonstrates the utility of the Pangeo software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the Irish Continental Shelf (ICS) region. It has involved the creation of a new wind data catalog for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.

This particular catalog data set (eooffshore_ics_ccmp_v02_1_nrt_wind.zarr) contains 2015-2021 Cross-Calibrated Multi-Platform (CCMP) v0.2.1.NRT 6-hourly wind products for the ICS region, where wind speed and direction are calculated from the uwnd and vwnd variables. The source data products are generated by Remote Sensing Systems (RSS). This CCMP data set was used in the EOOffshore project outputs presented (Scalable Offshore Wind Analysis With Pangeo) at the Meeting Exascale Computing Challenges with Compression and Pangeo 2022 EGU General Assembly session.

Example usage of the CCMP data set in EOOffshore:

CCMP Wind Data for Irish Continental Shelf region

Offshore Wind in Irish Areas Of Interest

Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation

Note:

This NCAR/UCAR Research Data Archive page states that the CCMP license is CC-BY-4.0. A separate CCMP data set has been previously used in the NASA CCMP Winds Pangeo Gallery notebook.

This dataset consists of high resolution sea surface winds data produced from Synthetic Aperture Radar (SAR) on board Sentinel-1A and Sentinel-1B satellites. The basic archive file is a netCDF-4 file containing SAR wind, land mask, and time and earth location information. Also included are maps of the SAR winds in GeoTIFF format. The product covers the geographic extent of the SAR image frame from which it was derived. These SAR-derived high resolution wind products are calculated from high resolution SAR images of normalized radar cross section (NRCS) of the Earth's surface. Backscattered microwave radar returns from the ocean surface are strongly dependent on wind speed and direction. When no wind is present, the surface of the water is smooth, almost glass-like. Radar energy will largely be reflected away and the radar cross section will be low. As the wind begins to blow, the surface roughens and surface waves begin to develop. As the wind continues to blow more strongly, the amplitude of the wave increases, thus, roughening the surface more. As the surface roughness increases, more energy is backscattered and NRCS increases. Moreover, careful examination of the wind-generated waves reveals that these surface wave crests are generally aligned perpendicular to the prevailing wind direction, suggesting a dependence of backscatter on the relative direction between the incident radar energy and the wind direction.

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length

In this dataset the anther's analysis is based on data from NREL about Solar & Wind energy generation by operation areas.

NASA Prediction of Worldwide Energy Resources

Solar

Monthly averages for global horizontal radiation over 22-year period (Jul 1983

• Jun 2013)

Wind

Monthly average wind speed at 50m above the surface of earth over a 30-year period (Jan 1984 - Dec 2013)Year: Averaged Over 10 to 15 years

COA = central operating area.

EOA = eastern operating area.

SOA = southern operating area.

WOA = western operating area. Source: NRELSource Link

Beacon Wind Met Buoy 1 (SW Corner) - Realtime Data. This dataset contains observations to support the development of the Beacon Wind Offshore Wind Project and to further understanding of metocean conditions off the coast of Massachusetts. The Beacon Wind Site Assessment Plan approved by the Bureau of Ocean Energy Management can be found at Beacon Wind | Bureau of Ocean Energy Management (boem.gov) Data is collected from one Floating LiDAR Buoy, two Wave/Met Buoys, and two Current Meter Moorings. The dataset includes measurements of sea temperature, current speed, current direction, waves, air temperature, atmospheric pressure, relative humidity, wind speed, and wind direction.