Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore.

Characterizing wind and precipitation conditions is essential for the durability and maintenance of wind turbine components. Precipitation-driven leading edge erosion of turbine blades has emerged as a significant concern, as it compromises aerodynamic performance and shortens blade lifespan. This study investigates wind and precipitation patterns across a large region of Europe, with a particular focus on the Southern Bight of the North Sea. Using ten years of ERA5 atmospheric reanalysis data, we analyze wind and precipitation conditions, and derive an erosion risk map based on the combined effects of precipitation and blade tip speed. To capture local-scale variability, we employ high-resolution WRF simulations over a three-year period to downscale ERA5 data for the Southern Bight region. These simulations are used to generate detailed seasonal maps of wind speed, precipitation, and erosion risk on a 3 km grid. Additionally, we compare precipitation estimates from ERA5, as well as from NASA's IMERG satellite product, NORA3 hindcast archive, and from the WRF model output against three Belgian weather stations. We emphasize the added value of high-resolution modeling in capturing precipitation heterogeneity that influences blade erosion rates. Integrating both large-scale and regional weather data supports site screening in early-stage wind farm planning, material selection in blade coatings, and maintenance prioritization, especially offshore, thus contributing to the cost-effectiveness of wind energy projects.