Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms
<p>Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) d...
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Copernicus Publications
2025-01-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/10/245/2025/wes-10-245-2025.pdf |
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| author | T. Ivanova T. Ivanova S. Porchetta S. Porchetta S. Buckingham G. Glabeke G. Glabeke J. van Beeck W. Munters |
| author_facet | T. Ivanova T. Ivanova S. Porchetta S. Porchetta S. Buckingham G. Glabeke G. Glabeke J. van Beeck W. Munters |
| author_sort | T. Ivanova |
| collection | DOAJ |
| description | <p>Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore.</p> |
| format | Article |
| id | doaj-art-f043835a5fd54f0c87f9dece6bad1954 |
| institution | Kabale University |
| issn | 2366-7443 2366-7451 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj-art-f043835a5fd54f0c87f9dece6bad19542025-01-23T11:07:22ZengCopernicus PublicationsWind Energy Science2366-74432366-74512025-01-011024526810.5194/wes-10-245-2025Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farmsT. Ivanova0T. Ivanova1S. Porchetta2S. Porchetta3S. Buckingham4G. Glabeke5G. Glabeke6J. van Beeck7W. Munters8Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, BelgiumDepartment of Engineering Technology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, BelgiumCivil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 1–290, Cambridge, MA 02139, United States of AmericaDepartment of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the NetherlandsResearch & Innovation, ENGIE Laborelec, Rodestraat 125, 1630 Linkebeek, BelgiumEnvironmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, BelgiumDepartment of Civil Engineering, Hydraulics Laboratory, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, BelgiumEnvironmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, BelgiumEnvironmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, Belgium<p>Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore.</p>https://wes.copernicus.org/articles/10/245/2025/wes-10-245-2025.pdf |
| spellingShingle | T. Ivanova T. Ivanova S. Porchetta S. Porchetta S. Buckingham G. Glabeke G. Glabeke J. van Beeck W. Munters Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms Wind Energy Science |
| title | Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms |
| title_full | Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms |
| title_fullStr | Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms |
| title_full_unstemmed | Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms |
| title_short | Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms |
| title_sort | improving wind and power predictions via four dimensional data assimilation in the wrf model case study of storms in february 2022 at belgian offshore wind farms |
| url | https://wes.copernicus.org/articles/10/245/2025/wes-10-245-2025.pdf |
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