Beyond Single Hazard Framework: Multi-Hazard Worst Case Scenarios from Ensemble Tropical Cyclone Forecasts

Ensemble forecasting is a powerful tool for supporting informed decision-making in managing multi-hazard risks associated with tropical cyclones (TCs). Although TC ensemble forecasts are widely used in operational numerical weather prediction systems, their potential for disaster prediction and management has not been fully exploited. Here we propose a novel, efficient, and practical method to extract meaningful Multi-Hazard Worst Case Scenarios (MHWCS) from a large ensemble TC forecast of 1000-members. We performed the ensemble atmospheric forecasting of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were serving as inputs for the JMA’s operational flood forecast model, as well as statistical storm surge and gust wind models. These models estimate river flooding, storm surge, and wind hazard intensities in Tokyo. By accounting for uncertainties in ensemble multi-hazard forecasts, we objectively demonstrate that Pareto-optimal solutions can effectively identify the meaningful MHWCS. These solutions illustrate complex trade-offs among competing hazard components across various forecast locations. While some identified MHWCS pose severe risks for a single hazard type or location, others present moderately high risks across multiple hazards and locations. This diversity in potential scenarios requires risk managers to prepare multiple response strategies for both imminent risks and post-disaster management. Our findings further underscore the importance of evaluating Pareto-optimal solutions to assist forecasters and risk managers in understanding how combinations of TC meteorological variables—such as track, translation speed, size, intensity, and rainfall—shape worst-case scenarios.