Combining hazard, exposure and vulnerability data to predict historical North Atlantic hurricane damage

Abstract. Hurricanes are among the most destructive natural hazards globally. Accurate risk assessment requires integrated hazard, exposure, and vulnerability information, yet the widely used Saffir–Simpson scale, while an effective public-communication tool, is based on a single hazard quantity (wind speed) and is not well correlated with historical economic losses, limiting its predictive value. This study develops a statistical model to predict economic damage from landfalling North Atlantic hurricanes using optimally weighted, normalised-rank variables representing hazard, exposure, and vulnerability. The model significantly reduces root-mean-square error between predicted and observed losses from U.S.$35.6 billion (when using landfall wind speed) to U.S.$7.0 billion, and substantially outperforms single-parameter predictions, including landfall wind speed maxima and central pressure minima. To improve communication of financial risk, we introduce a loss-based 'Hurricane Predictive Damage Scale' to more directly link hurricane characteristics to economic impacts. Our results demonstrate that integrating exposure and vulnerability data with hazard observations yields markedly better estimates of historical hurricane economic impacts, and this approach is readily applicable to future forecast hurricanes, allowing assessment of how damage from an imminent landfall may rank among historical events. This framework is transferable to other cyclone-prone regions and highlights the critical need for open exposure and vulnerability data to advance climate risk quantification and inform policy.