The map shows the average wind speed at an altitude of 225 m above ground. This was modelled throughout North Rhine-Westphalia in a resolution of 100 x 100 m and validated with the yields of existing wind turbines in North Rhine-Westphalia. The mean wind speed is an average of the wind speeds occurring over the year. The mean wind speed gives an indication of how a site is suitable for wind energy use.

Zipped collections of shapefiles are available in two spatial reference or coordinate systems: 1) Irish Transverse Mercator (ITM, EPSG:2157) 2) WGS 84 Web Mercator (EPSG:3857) The Sustainable Energy Authority of Ireland (SEAI) offers the same data in its Wind Atlas, a digital map of Ireland's wind energy resource (http://gis.seai.ie/wind). SEAI's 2003 datasets of wind characteristics assist wind energy planners, developers and policy makers. Background on 2003 wind maps The 2003 wind-mapping project was completed by ESB International and TrueWind Solutions for SEAI (then SEI). It predicted wind characteristics, at heights of 50m, 75m and 100m, spanning onshore and offshore. (Larger heights of 125m and 150m were later covered in SEAI’s 2013 wind-mapping project.) The resulting GIS maps cover onshore in 200m grids, and offshore in 400m grids. Generally, wind maps extend to 15km offshore, or occasionally 20km. About the 2003 methodology, it iterated a MesoMap system and a faster WindMap model through reducing grid sizes. MesoMap is built on MASS (Mesoscale Atmospheric Simulation System), a numerical weather model that embodied the fundamental physics of the atmosphere. Iterations through the nested grids accounted for local land elevation, land cover and roughness. Final iterations accounted for increased wind shear and reduced near-surface wind speed at less windy sites. The 2003 Wind-mapping Project Report is available here.

The map shows the average wind speed at 200 m above ground level. This was modelled throughout North Rhine-Westphalia in a resolution of 100 x 100 m and validated with the yields of existing wind turbines in North Rhine-Westphalia. The mean wind speed is an average of the wind speeds occurring over the year. The mean wind speed gives an indication of how a site is suitable for wind energy use.

The map shows the mean wind speed at a height of 200 m above ground. This was modeled across NRW with a resolution of 100 x 100 m and validated with the yields of existing wind turbines in NRW. The mean wind speed is an average of the wind speeds occurring over the year. The average wind speed gives an indication of how suitable a location is for wind energy use.

The following is excerpted from an unpublished report by Michael Brower (2004): "Using the MesoMap system, TrueWind has produced maps of mean wind speed in Indiana for heights of 30, 50, 70, and 100 m above ground, as well as a map of wind power at 50 m. TrueWind has also produced data files of the predicted wind speed frequency distribution and speed and energy by direction. The maps and data files are provided on a CD with the ArcReader software, which will enable users to view, print, copy, and query the maps and wind rose data. "The MesoMap system consists of an integrated set of atmospheric simulation models, databases, and computers and storage systems. At the core of MesoMap is MASS (Mesoscale Atmospheric Simulation System), a numerical weather model, which simulates the physics of the atmosphere. MASS is coupled to a simpler wind flow model, WindMap, which is used to refine the spatial resolution of MASS and account for localized effects of terrain and surface roughness. MASS simulates weather conditions over a region for 366 historical days randomly selected from a 15-year period. When the runs are finished, the results are input into WindMap. In this project, the MASS model was run on a grid spacing of 1.7 km and WindMap on a grid spacing of 200 m. "The wind maps show that the best wind resource in Indiana is found in the northcentral part of the state. The mean wind speed at 50 m height between Indianapolis, Kokomo, and Lafayette, and to the northwest of Lafayette, is predicted to be in the range of 6.5 to 7 m/s, and the mean wind power is predicted to be about 250 to 350 W/m2, or NREL class 2 to 3. In the rest of northern Indiana, the wind speed tends to be around 0.5 m/s lower, and the wind power is a solid class 2. In southern Indiana, a wind speed of 4.5 to 6 m/s and a wind power class of 1 to 2 prevails. The main reason for this wind resource distribution pattern is that the land is much more forested in the southern half of the state than in the northern half. Topography also plays a role, as does the track of the jet stream."

The map shows the mean wind speed at a height of 225 m above ground. This was modeled across NRW with a resolution of 100 x 100 m and validated with the yields of existing wind turbines in NRW. The mean wind speed is an average of the wind speeds occurring over the year. The average wind speed gives an indication of how suitable a location is for wind energy use.

Mean average wind speeds in metres per second (m/s) at 100 m height. These datasets cover the land area and coastal waters of Ireland. Data Compilation was completed in 2003.

Zipped collections of shapefiles are available in two Spatial reference or coordinate systems: 1) Irish transverse Mercator (ITM, EPSG:2157) 2) WGS 84 Web Mercator (EPSG:3857)

The Sustainable Energy Authority of Ireland (SEAI) offers the same data in its Wind Atlas, a digital map of Ireland’s wind energy resource (http://gis.seai.ie/wind). SEAI’s 2003 datasets of wind characteristics assist wind energy Planners, developers and policymakers.

Background on 2003 wind maps The 2003 wind-mapping project was completed by ESB International and TrueWind Solutions for SEAI (then SEI). It predicted wind characteristics, at heights of 50 m, 75 m and 100 m, Spanning onshore and offshore. (Larger heights of 125 m and 150 m were later covered in SEAI’s 2013 wind-mapping project.) The resulting GIS maps cover onshore in 200 m grids, and offshore in 400 m grids. Generally, wind maps extend to 15 km offshore, or occasionally 20 km.

About the 2003 methodology, it iterated a MesoMap system and a faster WindMap model through reducing grid sizes. MesoMap is built on MASS (Mesoscale Atmospheric Simulation System), a numerical weather model that embodied the fundamental physics of the atmosphere. Iterations through the nested grids accounted for local land elevation, land cover and roughness. Final iterations accounted for increased wind shear and reduced near-surface wind speed at less Windy sites. The 2003 Wind-mapping Project Report is available here.

Potential card for renewable electricity production. This contains the map with average wind speeds at 100m altitude.

Average monthly wind speed and direction grids across Australia. The data are based on the period 1st January 2004 – 31st December 2008. Computer model are used as part of the weather forecasting …Show full descriptionAverage monthly wind speed and direction grids across Australia. The data are based on the period 1st January 2004 – 31st December 2008. Computer model are used as part of the weather forecasting process. These models produce a snapshot of the current state of the atmosphere before they can produce forecasts. This snapshot is often called an 'analysis'. The analysis and subsequent predictions are based on data (ground stations, upper air observations, satellites, ships, buoys etc.) from the world's national meteorological services, including Australia. Data are fed into computers, and the wind field and the various other meteorological data fields are calculated at various elevations or levels to represent the physical process or dynamics of the full depth of the atmosphere. The monthly averages for wind were calculated from the daily computer generated analyses. The 10 metre surface wind field from the model, provided as gridded data, was used to develop these wind climate maps.

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.

Mean average wind speeds in metres per second (m/s) at 20m above ground level. These datasets cover the geographic land area of Ireland and Irish Internal Waters. Data compilation was completed in 2013, by analysing measurements taken during 2001--2010.

Zipped collections of shapefiles are available in two spatial reference or coordinate systems:
1) Irish Transverse Mercator (ITM, EPSG:2157)
2) WGS 84 Web Mercator (EPSG:3857)

The Sustainable Energy Authority of Ireland (SEAI) offers the same data in its Wind Atlas, a digital map of Ireland's wind energy resource (http://gis.seai.ie/wind). SEAI's wind speed datasets assist wind energy planners, developers and policy makers.

The following is excerpted from an unpublished report by Michael Brower (2004): "Using the MesoMap system, TrueWind has produced maps of mean wind speed in Indiana for heights of 30, 50, 70, and 100 m above ground, as well as a map of wind power at 50 m. TrueWind has also produced data files of the predicted wind speed frequency distribution and speed and energy by direction. The maps and data files are provided on a CD with the ArcReader software, which will enable users to view, print, copy, and query the maps and wind rose data. "The MesoMap system consists of an integrated set of atmospheric simulation models, databases, and computers and storage systems. At the core of MesoMap is MASS (Mesoscale Atmospheric Simulation System), a numerical weather model, which simulates the physics of the atmosphere. MASS is coupled to a simpler wind flow model, WindMap, which is used to refine the spatial resolution of MASS and account for localized effects of terrain and surface roughness. MASS simulates weather conditions over a region for 366 historical days randomly selected from a 15-year period. When the runs are finished, the results are input into WindMap. In this project, the MASS model was run on a grid spacing of 1.7 km and WindMap on a grid spacing of 200 m. "The wind maps show that the best wind resource in Indiana is found in the northcentral part of the state. The mean wind speed at 50 m height between Indianapolis, Kokomo, and Lafayette, and to the northwest of Lafayette, is predicted to be in the range of 6.5 to 7 m/s, and the mean wind power is predicted to be about 250 to 350 W/m2, or NREL class 2 to 3. In the rest of northern Indiana, the wind speed tends to be around 0.5 m/s lower, and the wind power is a solid class 2. In southern Indiana, a wind speed of 4.5 to 6 m/s and a wind power class of 1 to 2 prevails. The main reason for this wind resource distribution pattern is that the land is much more forested in the southern half of the state than in the northern half. Topography also plays a role, as does the track of the jet stream."

Map of average wind speeds at 180 m altitude above ground as average from 2001 to 2020 in a resolution of 10 x 10 m.

DTU Global Wind Atlas: onshore and 30 km offshore wind climate dataset accounting for high resolution terrain effects.The Global Wind Atlas provides a high resolution wind climatology at 50, 100, 200m hub heights above the surface for the whole world (onshore and 30 km offshore). These layers have been produced using microscale modelling in the Wind Atlas Analysis and Application Program (WAsP) and capture small scale spatial variability of winds speeds due to high resolution orography (terrain elevation), surface roughness and surface roughness change effects. The layers shared through the IRENA Global Atlas are served at 1km spatial resolution. The full Atlas contains data at a higher spatial resolution of 250 m, some of the IRENA Global Atlas tools access this data for aggregated statistics.Original website:http://globalwindatlas.com/Data quality and validation:The layers have been produced by the Technical University of Denmark (DTU), Department of Wind Energy (DTU Wind Energy), using state-of-the art scientifically verified models and methods (Report accessible: http://globalwindatlas.com/).This data is classified as POLICY+BUSINESS, according to IRENA’s classification framework for solar and wind resource maps (http://www.irena.org/DocumentDownloads/Publications/Global%20Atlas_Data%20_Quality.pdf)- POLICY: The information provided is meant to inform high-level policy debate (identification of opportunity areas for further prospection, preliminary assessment of technical potentials), or to perform market screening (cross referencing the resource information with policy information). It is suitable for decision-making activities, excluding financial commitments.- +BUSINESS: the information provided is a sub-sample of a dataset of better spatial and/or temporal resolution than that available from the Global Atlas, and that of sufficient magnitude to initiate business-related activities, (e.g.,. kilometre (km) or less than a-kilometre, hourly data). Detailed information can be supplied by the owner of the data.- Detailed data quality information: http://globalatlas.irena.org/dqif/DQIF.aspx?datasetid=5039Terms of use:By using this dataset, the user accepts the following Terms and Conditions:- USE OF THE DATASET: Terms of use of the Global Wind Atlas: http://globalwindatlas.com/https://globalatlas2.masdar.ac.ae/geoserver/gwa/wms- USE OF THE IRENA GLOBAL ATLAS: Terms of use of the Global Atlas for Renewable Energy shown here: http://irena.masdar.ac.ae/clients/irena/legal.html

WMS of the Canary Islands Wind Resource that has been prepared by the Instituto Tecnológico de Canarias (ITC) and describes the main characteristics of the existing wind (speed, direction and other parameters) for specific coordinates of points of the Canary archipelago. The study points are integrated into a 100 m resolution mesh, both in the North-South and East-West directions. The information for each point is detailed in a line that describes the most likely wind behavior at that point, and sorted by the respective UTM coordinates. The information is only estimative, since wind data have not been measured, but calculated. From a simulation based on the use of models, maps of average wind speeds, speed distribution curve, wind roses and turbulence intensity at 40, 60 and 80 m high have been created for the Canary archipelago. This group therefore has the following layers: Wind roses, turbulence intensity, wind resource or average wind speed and rugosity or influence of obstacles and terrain contours on the wind.

Date of data: year 2007.

Over the past two decades, the average wind speed in the United Kingdom has remained relatively stable. In 2024, the average wind speed in the UK was 8.4 knots. Speeds peaked during this period in 2015 at 9.4 knots, before falling to 8.4 knots the following year. One knot is equivalent to one nautical mile per hour. Overall, wind speeds have mostly remained between eight and nine knots, dropping to a low of 7.8 in 2010. The first and fourth quarters were the windiest Since 2010, the first and fourth quarters of each year generally recorded the highest wind speeds. The highest quarterly wind speed averages occurred in the first quarter of 2020, with speeds of approximately 11.5 knots. Between 2015 and 2023, the most noticeable deviation from the 10-year mean was recorded in February 2020. In this month wind speeds were 4.2 knots higher than normal. Optimal wind conditions for wind energy The United Kingdom has some of the best wind conditions in Europe for wind power, so it is no surprise that it plays an important role in the country's energy mix. As of 2023, there were 39 offshore wind farms operating in the UK, by far the most in Europe. Furthermore, in the same year, offshore wind power additions in the UK reached 1.14 gigawatts.

This map illustrates the variety of average project capacity sizes across the United States and within the state of California, for wind projects whose total nameplate capacity is greater than or equal to 1 MW (20 CCR § 1385). Average project capacity is a function of the statewide, or countywide, nameplate capacity and number of projects within the defined area. The absence of projects in the southeastern United States is arbitrable to low average wind speeds (United States - Annual Average Wind Speed, AWS True power and National Renewable Energy Lab), and insufficient hurricane-resistant technology. 1 August 2019 Produced by the California Energy Commission Projection: NAD 1983 (2011) USA Congruous Albers Equal-Area Conic Authors: Dylan Kojimoto (916) 651-0477, John Hingtgen (916) 657-4046, Brandon Davis Data: Energy Information Administrator (EIA-860) and Wind Performance Reporting System (WPRS)

The map shows the mean wind speed at 150 m altitude above ground. This was modelled throughout NRW in a resolution of 100 x 100 m and validated with the yields of existing wind turbines in NRW. The mean wind speed is an average of the wind speeds occurring over the year. The mean wind speed gives an indication of how a location is suitable for wind energy use.

This map illustrates the variety of median plant capacity sizes across the United States and California counties for wind plants whose total nameplate capacity is greater than or equal to 1 MW. The absence of plants in the southeastern United States and California is attributable to low average wind speeds (United States - Annual Average Wind Speed, AWS Truepower and National Renewable Energy Lab). Hurricane strength winds are not usable with current technology. Median plant capacity is a function of the statewide capacity and the number of plants within the defined area.

This map illustrates the variety of median project capacity sizes across the United States and California counties for wind projects whose total nameplate capacity is greater than or equal to 1 MW. The absence of projects in the southeastern United States is attributable to low average wind speeds (United States - Annual Average Wind Speed, AWS Truepower and National Renewable Energy Lab). Hurricane strength winds are not usable with current technology. Median project capacity is a function of the statewide capacity and the number of projects within the defined area. August 3, 2022; Produced by the California Energy Commission; Projection: NAD 1983 (2011) Contiguous USA Albers; Data: Energy Information Administration (EIA-860) and the Wind Generation Reporting System (WPRS). For more information, please contact Rebecca Vail (916)477-0738, or John Hingtgen (916)510-9747.