Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-607-2023
https://doi.org/10.5194/wes-8-607-2023
Research article
 | 
28 Apr 2023
Research article |  | 28 Apr 2023

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Nicola Bodini, Simon Castagneri, and Mike Optis

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Cited articles

Alessandrini, S., Sperati, S., and Pinson, P.: A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data, Appl. Energ., 107, 271–280, https://doi.org/10.1016/j.apenergy.2013.02.041, 2013. a
AWS Truepower: AWS Truepower Loss and Uncertainty Methods, Albany, NY, https://www.awstruepower.com/assets/AWS-Truepower-Loss-and-Uncertainty-Memorandum-5-Jun-2014.pdf (last access: 1 October 2022), 2014. a
Bodini, N. and Optis, M.: How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?, J. Phys.: Conf. Ser., 1618, 062037, https://doi.org/10.1088/1742-6596/1618/6/062037, 2020a. a
Bodini, N. and Optis, M.: The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds, Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, 2020b. a
Bodini, N. and Optis, M.: WRF nameless for NREL's Mid-Atlantic WRF simulations, Zenodo [code], https://doi.org/10.5281/zenodo.7814365, 2023. a
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Short summary
The National Renewable Energy Laboratory (NREL) has published updated maps of the wind resource along all US coasts. Given the upcoming offshore wind development, it is essential to quantify the uncertainty that comes with the modeled wind resource data set. The paper proposes a novel approach to quantify this numerical uncertainty by leveraging available observations along the US East Coast.
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