Preprints
https://doi.org/10.5194/wes-2023-177
https://doi.org/10.5194/wes-2023-177
07 Mar 2024
 | 07 Mar 2024
Status: this preprint is currently under review for the journal WES.

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, Jeroen van Beeck, and Wim Munters

Abstract. Accurate wind and power predictions from numerical models are crucial for wind farm operation and management. This study explores how these predictions can be improved by assimilating local offshore data into a numerical weather prediction model, while simultaneously taking into account the presence of neighboring wind farms. The focus is on the Belgian-Dutch wind farm cluster located in the Southern Bight of the North Sea. Our results show that, for the current case study with extreme weather conditions, the assimilation of upstream data reduces mean absolute errors of wind speed, wind direction, and power predictions, up to 2.7 times in comparison to simulations without any assimilation. This approach can be useful for forecasting purposes in short- to mid-term horizons, as well as for a long-term refined reanalysis of various weather conditions and events.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Jeroen van Beeck, and Wim Munters
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Jeroen van Beeck, and Wim Munters

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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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