Aditya N. Mishra

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.

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.

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.

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.

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.

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.

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.

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.

Winter windstorms rank as one of Europe's deadliest and most damaging natural disasters. To model the impacts of these windstorms, surface wind data can be incorporated into climate risk models to derive estimates of natural hazard-related impacts on natural or socio-economic systems. In CLIMADA, risk from a natural hazard can be modelled as the convolution between three components - hazard, exposure, and vulnerability. The vulnerability component links the hazard and exposure components to give total impact that can be approximated through functional relationships called vulnerability curves (or impact functions in CLIMADA). Advancing the science of impact estimation from windstorms is imperative for mitigation and management of changing climate risks, and this relies on appropriate calibration of the vulnerability curve. To this end, in this study, we calibrate a popular impact function from Schwierz et al. (2010) using impact data from two types of sources: open-source (EM-DAT/XWS) and proprietary (PERILS). Results indicate substantial differences between the calibrated vulnerability curves and highlight the importance of the type of recorded disaster data used in calibration. Furthermore, for each of the aforementioned calibration cases, we discuss the uncertainties associated with the use of different cost functions and optimization techniques in the calibration process. The study brings forth how data and method choices influence vulnerability curves, helping better understand modelling uncertainty and support the development of more reliable tools for climate risk assessment and adaptation.

Winter windstorms rank as one of Europe's deadliest and most damaging natural disasters. To model the impacts of these windstorms, surface wind data can be incorporated into climate risk models to derive estimates of natural hazard-related impacts on natural or socio-economic systems. In CLIMADA, risk from a natural hazard can be modelled as the convolution between three components - hazard, exposure, and vulnerability. The vulnerability component links the hazard and exposure components to give total impact that can be approximated through functional relationships called vulnerability curves (or impact functions in CLIMADA). Advancing the science of impact estimation from windstorms is imperative for mitigation and management of changing climate risks, and this relies on appropriate calibration of the vulnerability curve. To this end, in this study, we calibrate a popular impact function from Schwierz et al. (2010) using impact data from two types of sources: open-source (EM-DAT/XWS) and proprietary (PERILS). Results indicate substantial differences between the calibrated vulnerability curves and highlight the importance of the type of recorded disaster data used in calibration. Furthermore, for each of the aforementioned calibration cases, we discuss the uncertainties associated with the use of different cost functions and optimization techniques in the calibration process. The study brings forth how data and method choices influence vulnerability curves, helping better understand modelling uncertainty and support the development of more reliable tools for climate risk assessment and adaptation.