Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-245-2025
https://doi.org/10.5194/wes-10-245-2025
Research article
 | 
23 Jan 2025
Research article |  | 23 Jan 2025

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

Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Gertjan Glabeke, Jeroen van Beeck, and Wim Munters

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Cited articles

Ali, K., Schultz, D. M., Revell, A., Stallard, T., and Ouro, P.: Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea, Mon. Weather Rev., 151, 2333–2359, https://doi.org/10.1175/mwr-d-23-0006.1, 2023. a
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Archer, C. L., Wu, S., Ma, Y., and Jiménez, P. A.: Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model, Mon. Weather Rev., 148, 4823–4835, https://doi.org/10.1175/mwr-d-20-0097.1, 2020. a, b
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Barker, D. M., Huang, W., Guo, Y.-R., Bourgeois, A. J., and Xiao, Q. N.: A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results, Mon. Weather Rev., 132, 897–914, https://doi.org/10.1175/1520-0493(2004)132<0897:atvdas>2.0.co;2, 2004. a
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Short summary
This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
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