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
Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, Belgium
Department of Engineering Technology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Sara Porchetta
Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 1–290, Cambridge, MA 02139, United States of America
Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Sophia Buckingham
Research & Innovation, ENGIE Laborelec, Rodestraat 125, 1630 Linkebeek, Belgium
Gertjan Glabeke
Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, Belgium
Department of Civil Engineering, Hydraulics Laboratory, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium
Jeroen van Beeck
Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Chau. de Waterloo 72, 1640 Rhode-Saint-Genèse, Belgium
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.
This study explores how wind and power predictions can be improved by introducing local forcing...
