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.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.