Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

Liu, Ye; Juliano, Timothy W.; Krishnamurthy, Raghavendra; Gaudet, Brian J.; Lee, Jungmin

doi:https://doi.org/10.5194/wes-10-483-2025

Articles | Volume 10, issue 2

https://doi.org/10.5194/wes-10-483-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-10-483-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 2

Research article

|

24 Feb 2025

Research article |

| 24 Feb 2025

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee

Data sets

California - Leosphere Windcube 866 (120), Humboldt/Reviewed Data Atmosphere to Electrons (A2e) https://doi.org/10.21947/1783809

California - Leosphere Windcube 866 (130), Morro Bay/Reviewed Data Atmosphere to Electrons (A2e) https://doi.org/10.21947/1959721

NOAA High-Resolution Rapid Refresh (HRRR) Model NOAA https://registry.opendata.aws/noaa-hrrr-pds

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

Short summary

Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.