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

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

doi:https://doi.org/10.5194/wes-10-245-2025

Articles | Volume 10, issue 1

https://doi.org/10.5194/wes-10-245-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-10-245-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 1

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

Data sets

NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive National Centers for Environmental Prediction, National Weather Service, NOAA, and US Department of Commerce https://doi.org/10.5065/D65D8PWK

Model code and software

A Description of the Advanced Research WRF Model Version 4.3 W. C. Skamarock et al. https://doi.org/10.5065/1DFH-6P97

wrf-python (Version 1.3.4) [Software] B. Ladwig https://doi.org/10.5065/D6W094P1

WRF Version 4.5.1 (Bug-fix Release) Community developed code https://github.com/wrf-model/WRF/releases/tag/v4.5.1

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.