Improving Europe-wide windstorm damage modeling using insurance loss data

Abstract. Winter windstorms are among Europe’s deadliest and most damaging natural hazards. In a changing climate, reliably estimating and projecting their impacts is essential for effective risk management. Such risk is often modeled as the intersection between hazard, exposure, and vulnerability, which are linked through functional relationships known as vulnerability curves (or damage models). The Schwierz et al. (2010) damage model is a widely used open-source standard for European windstorms, but its original calibration is based on a limited set of historical UK storms. In this study, this model is calibrated against recent loss data from the PERILS database (1999–2024) within the CLIMADA open-source framework, across 12 European countries. Calibration is conducted independently using two cost functions: Root Mean Squared Error (RMSE) and Root Mean Squared Logarithmic Error (RMSLE), enabling a systematic comparison of their influence on the optimised damage parameter and derived risk metrics. The default model is found to systematically underestimate losses across Europe, and a single pan-European model cannot capture the distinct vulnerability profiles of individual countries. By calibrating the model against PERILS losses, a new set of country-specific damage functions is developed, which reflect spatial heterogeneities in vulnerability. In addition, the analysis demonstrates that the chosen loss function (RMSE versus RMSLE) fundamentally shapes the calibrated curves and the resulting risk profile, underscoring that calibration metric is itself a key modeling decision. The results offer practical guidance for calibrating damage models and support more rigorous climate risk assessment.