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

This is the authors' abstract. We don't add key points for this paper.

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.