Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-821-2024
https://doi.org/10.5194/wes-9-821-2024
Research article
 | 
08 Apr 2024
Research article |  | 08 Apr 2024

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée

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Cited articles

Aird, J., Barthelmie, R., Shepherd, T., and Pryor, S.: WRF-Simulated springtime low-level jets over Iowa: Implications for Wind Energy, J. Phys. Conf. Ser., 1618, 062020, https://doi.org/10.1088/1742-6596/1618/6/062020, 2020. a
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: WRF-simulated low-level jets over Iowa: characterization and sensitivity studies, Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, 2021. a
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: Occurrence of Low-Level Jets over the Eastern US Coastal Zone at Heights Relevant to Wind Energy, Energies, 15, 445, https://doi.org/10.3390/en15020445, 2022. a, b, c
Amador, J. A.: The intra-Americas sea low-level jet: Overview and future research, Ann. N. Y. Acad. Sci., 1146, 153–188, https://doi.org/10.1196/annals.1446.012, 2008. a
American Clean Power Association: U.S. Offshore Wind Power Economic Impact Assessment, https://cleanpower.org/wp-content/uploads/2021/01/AWEA_Offshore-Wind-Economic-ImpactsV3.pdf (last access: 4 April 2024), 2020. a
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
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