Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2024
Research article |
| 08 Apr 2024
| 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
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Jeanie A. Aird
CORRESPONDING AUTHOR
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Stefan Ivanell
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Heiner Körnich
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Ville Vakkari
Finnish Meteorological Institute, Helsinki, Finland
Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa
Rebecca J. Barthelmie
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Sara C. Pryor
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
Erik Sahlée
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
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Cited
15 citations as recorded by crossref.
- Wind-Regime and Spatial-Transfer Performance of a Station-Trained 5 km Wind-Speed Correction over Hainan Island J. Xu et al. https://doi.org/10.3390/atmos17090846
- Balancing Resource Potential and Investment Costs in Offshore Wind Projects: Evidence from Northern Colombia A. Ospino-Castro et al. https://doi.org/10.3390/en18226003
- On the Interaction of Tropical Easterly Waves and the Caribbean Low-Level Jet Using Observed, ERA5 and WWLLN Data over the Intra-Americas Seas During OTREC 2019 J. Amador et al. https://doi.org/10.3390/meteorology5010006
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall https://doi.org/10.5194/wes-10-143-2025
- Assessing the impacts of spatial constraints on the techno-economic potential of offshore wind energy: A case study of the Caribbean Sea, Colombia L. Suarez Bermudez et al. https://doi.org/10.1016/j.ecmx.2026.101703
- Process-oriented evaluation of machine learning and physics-based models for wave parameter prediction under extreme conditions in a fetch-limited sea K. Dubois et al. https://doi.org/10.1016/j.oceaneng.2026.128227
- Long-Term (2015–2024) Daily PM2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition J. Jiang et al. https://doi.org/10.3390/rs17091632
- Segmented Bias Correction of ERA5 100 m Wind Speed for Wind-Resource Assessment in Complex Terrain Y. Xu et al. https://doi.org/10.3390/en19194625
- Detecting Low-Level Jets over the Belgian North Sea from Sparse In-Situ Measurements using Temporal Convolutional Networks G. Glabeke et al. https://doi.org/10.1088/1742-6596/3224/2/022044
- Offshore Wind Resource Assessment and Wind Farm Optimization Using Machine Learning and CFD Modelling C. Temiz et al. https://doi.org/10.3390/wind6030051
- 双向长短期记忆网络在激光雷达风廓线预测的应用 廉. Lian Wenchao et al. https://doi.org/10.3788/AOS240891
- Tall wind profile validation of ERA5, NORA3, and NEWA datasets using lidar observations E. Cheynet et al. https://doi.org/10.5194/wes-10-733-2025
- Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends A. Durap https://doi.org/10.1007/s11852-025-01108-y
- Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1 B. Olsen et al. https://doi.org/10.5194/gmd-18-4499-2025
- Dimensionless learning-based prediction of 50–300 m offshore wind profiles from wind-free ocean surface observations M. Nabil et al. https://doi.org/10.1016/j.oceaneng.2026.127597
15 citations as recorded by crossref.
- Wind-Regime and Spatial-Transfer Performance of a Station-Trained 5 km Wind-Speed Correction over Hainan Island J. Xu et al. https://doi.org/10.3390/atmos17090846
- Balancing Resource Potential and Investment Costs in Offshore Wind Projects: Evidence from Northern Colombia A. Ospino-Castro et al. https://doi.org/10.3390/en18226003
- On the Interaction of Tropical Easterly Waves and the Caribbean Low-Level Jet Using Observed, ERA5 and WWLLN Data over the Intra-Americas Seas During OTREC 2019 J. Amador et al. https://doi.org/10.3390/meteorology5010006
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall https://doi.org/10.5194/wes-10-143-2025
- Assessing the impacts of spatial constraints on the techno-economic potential of offshore wind energy: A case study of the Caribbean Sea, Colombia L. Suarez Bermudez et al. https://doi.org/10.1016/j.ecmx.2026.101703
- Process-oriented evaluation of machine learning and physics-based models for wave parameter prediction under extreme conditions in a fetch-limited sea K. Dubois et al. https://doi.org/10.1016/j.oceaneng.2026.128227
- Long-Term (2015–2024) Daily PM2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition J. Jiang et al. https://doi.org/10.3390/rs17091632
- Segmented Bias Correction of ERA5 100 m Wind Speed for Wind-Resource Assessment in Complex Terrain Y. Xu et al. https://doi.org/10.3390/en19194625
- Detecting Low-Level Jets over the Belgian North Sea from Sparse In-Situ Measurements using Temporal Convolutional Networks G. Glabeke et al. https://doi.org/10.1088/1742-6596/3224/2/022044
- Offshore Wind Resource Assessment and Wind Farm Optimization Using Machine Learning and CFD Modelling C. Temiz et al. https://doi.org/10.3390/wind6030051
- 双向长短期记忆网络在激光雷达风廓线预测的应用 廉. Lian Wenchao et al. https://doi.org/10.3788/AOS240891
- Tall wind profile validation of ERA5, NORA3, and NEWA datasets using lidar observations E. Cheynet et al. https://doi.org/10.5194/wes-10-733-2025
- Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends A. Durap https://doi.org/10.1007/s11852-025-01108-y
- Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1 B. Olsen et al. https://doi.org/10.5194/gmd-18-4499-2025
- Dimensionless learning-based prediction of 50–300 m offshore wind profiles from wind-free ocean surface observations M. Nabil et al. https://doi.org/10.1016/j.oceaneng.2026.127597
Saved (final revised paper)
Latest update: 08 Oct 2026
Short summary
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.
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of...
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