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 v1.1WIND 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.

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 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.

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 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.

Mean average wind speeds in metres per second (m/s) at 75 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.

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.

Abstract:Monash University under commission of Geoscience Australia produced an offshore wind capacity factor map assessed at a 150m hub height applying the Bureau of Meteorology 10 year (2009-2018) “Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia” (BARRA) hindcast model. The wind capacity factor has been calculated using the bounding curve of all scaled power curves for wind turbines available within the Open Energy Platform as of 2021. Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines and are available in this web map service.Lineage:The Monash University project report (Offshore wind capacity factor maps - evaluating Australia's offshore wind resources potential) which is associated to this metadata record, details the method used to produce the offshore wind capacity factor maps. The method included geospatial alignment of the raw data, wind speed interpolation at 150m, calculation of the mean and standard deviation for hourly wind speeds at 150m from 2009 to 2018, the application of the methods of moments technique to calculate the shape and scale parameter of the wind Weibull distribution and calculation of a bounding curve for the power curves of wind turbines.The maximum offshore wind generation potential was calculated through the generation of a bounding curve for the currently, as of 2021, wind turbine power curves within the Open Energy Platform. The Weibull distribution parameters and the bounding curve were then combined to calculate the wind capacity factor values.Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines.© Commonwealth of Australia (Geoscience Australia) 2022.Downloads and Links:Web ServicesOffshore Wind Capacity Factor Maps (Map Server)Offshore wind Capacity Factor Maps (WMS)Downloads available from the expanded catalogue link, belowMetadata URL:https://pid.geoscience.gov.au/dataset/ga/146703

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 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 125 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.

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

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.

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.

The wind resource has been mapped on a 3 km x 3 km grid across Victoria. Average annual wind speeds have been modelled using the WindScape wind resource mapping tool that was developed by the Wind Energy Research Unit of CSIRO Land and Water in 2002. WindScape uses atmospheric data, and regional topography to model the wind resource at 65 metres above ground level to a resolution of 3 kilometres. The resolution of the modelled wind resource means that it does not incorporate the effects of local landscape features smaller than 3 kilometres in size, like small hills and ridges. This dataset is derived from the same data used to create the overview map of the Victorian wind atlas published by the Sustainable Energy Authority Victoria, c2003

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

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)

This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.