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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-33', Anonymous Referee #1, 20 May 2021
    • AC1: 'Reply on RC1', Nicola Bodini, 27 Jul 2021
  • RC2: 'Comment on wes-2021-33', Anonymous Referee #2, 08 Jul 2021
    • AC2: 'Reply on RC2', Nicola Bodini, 27 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nicola Bodini on behalf of the Authors (27 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Oct 2021) by Joachim Peinke
ED: Publish as is (06 Oct 2021) by Joachim Peinke(Chief Editor)
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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.