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

Viewed

Total article views: 4,930 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,286 1,486 158 4,930 142 178
  • HTML: 3,286
  • PDF: 1,486
  • XML: 158
  • Total: 4,930
  • BibTeX: 142
  • EndNote: 178
Views and downloads (calculated since 07 Mar 2024)
Cumulative views and downloads (calculated since 07 Mar 2024)

Viewed (geographical distribution)

Total article views: 4,930 (including HTML, PDF, and XML) Thereof 4,710 with geography defined and 220 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 01 Oct 2026
Download
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.
Share
Altmetrics
Final-revised paper
Preprint