Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 6
Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 6
Research article
 | 
03 Nov 2021
Research article |  | 03 Nov 2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini

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Short summary
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.
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