Copula-based joint distributions of rain and wind for leading edge erosion risk atlas

Evaluating the risk of leading edge erosion on wind turbine blades requires accurate characterization of wind and rain climates, as they determine the amount of rain impinging the blades. However, existing fatigue lifetime models often assume statistical independence between wind speed and rain intensity, potentially underestimating erosion risks. This study introduces a copula-based framework to explicitly model their dependence using historical data from the British Isles and surrounding seas. The methodology is used to generate a probabilistic erosion parameter atlas containing site-specific marginal distributions and copula parameters. The parameter atlas enables fast and tailored estimates of the probabilistic incubation period, allowing for site-specific reliability analysis based on custom turbine operation characteristics and blade coating materials. The framework also supports the assessment of erosion-safe operation feasibility by quantifying the trade-off between fatigue life extension and energy production loss. Results reveal that neglecting the dependence structure can lead to overestimation of the incubation period by up to 90% in regions where the wind–rain correlation is high, particularly in offshore and coastal areas. A clear positive correlation between wind speed and rain intensity is observed, with the effect being even more pronounced in dry conditions, where wind speed on average is 50% higher when it rains 10 mm/hr compared to no rain, highlighting the critical need to incorporate wind–rain dependence into erosion risk assessments.