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.

Roller element bearings present in the intermediate and high-speed stages of wind turbine gearboxes operate in dynamic working conditions and in some cases may fail within 30% or less of their designed lifetime. Upon investigation, it has been identified that these premature failures happen due to a peculiar failure mode associated with formation of white etching cracks (WEC). This continues to be a great challenge for the wind energy operators as it leads to an increase of maintenance and operation costs in addition to long wind turbine downtime. Therefore, the industry is in dire need of a lifetime prediction methodology that could take in multi-scale inputs ranging from bearing loads at the system level down to the level of bearing material properties at the microscopic level. This work summarizes the overall approach of a project that aims towards an integrated framework which links load data from the bearings and microstructure related non-metallic inclusion statistics from bearing steels, to predict a material based probability of failure. The interlink between both aspects is a numerical rolling contact fatigue (RCF) framework based on finite element analysis, which includes multi-scale data as an input to calculate rolling contact fatigue damage. The outcome will help the wind industry to better predict bearing failures.