Articles | Volume 6, issue 6
Wind Energ. Sci., 6, 1363–1377, 2021
https://doi.org/10.5194/wes-6-1363-2021
Wind Energ. Sci., 6, 1363–1377, 2021
https://doi.org/10.5194/wes-6-1363-2021

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 et al.

Data sets

US Offshore Wind Resource data for 2000-2019 Nicola Bodini, Mike Optis, Michael Rossol, and Alex Rybchuk https://doi.org/10.25984/1821404

Model code and software

Parallel Analog Ensemble Weiming Hu https://weiming-hu.github.io/AnalogsEnsemble

nbodini/ML_UQ_offshore: ML_UQ_offshore (Version v1) Nicola Bodini https://doi.org/10.5281/zenodo.5618470

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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.