Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 6
Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 6
Research article
 | 
12 Jun 2025
Research article |  | 12 Jun 2025

A new gridded offshore wind profile product for US coasts using machine learning and satellite observations

James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung

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Cited articles

Atlantic Shores Offshore Wind: Atlantic shores offshore wind, ASOW-4, winds profile, Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) [data set], https://erddap.maracoos.org/erddap/tabledap/AtlanticShores_ASOW-4_wind.html (last access: 26 February 2024), 2023a. 
Atlantic Shores Offshore Wind: Atlantic shores offshore wind, ASOW-4, timeseries data, Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) [data set], https://erddap.maracoos.org/erddap/tabledap/AtlanticShores_ASOW-4_timeseries.html (last access: 26 February 2024), 2023b. 
Atlantic Shores Offshore Wind: Atlantic shores offshore wind, ASOW-6, winds profile, Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) [data set], https://erddap.maracoos.org/erddap/tabledap/AtlanticShores_ASOW-6_wind.html (last access: 1 March 2024), 2023c. 
Atlantic Shores Offshore Wind: Atlantic shores offshore wind, ASOW-6, timeseries data, Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) [data set], https://erddap.maracoos.org/erddap/tabledap/AtlanticShores_ASOW-6_timeseries.html (last access: 1 March 2024), 2023d. 
Baquero, L., Torio, H., and Leask, P.: Machine learning algorithms for vertical wind speed data extrapolation: Comparison and performance using mesoscale and measured site data, Energies, 15, 5518, https://doi.org/10.3390/en15155518, 2022. 
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A machine learning model is developed using lidar stations around US coasts to extrapolate wind speed profiles up to the hub heights of wind turbines from surface wind speeds. Independent validation shows that our model vastly outperforms traditional methods for vertical wind extrapolation. We produce a new long-term gridded dataset of wind speed profiles from 20 to 200 m at 0.25° and 6-hourly resolution from 1987 to the present by applying this model to the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Information (NCEI) Blended Seawinds product.
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