Advancing Multi-Hazard Analysis: 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). While TC ensemble forecasts are widely utilized in operational numerical weather prediction systems, their potential for disaster prediction remains underutilized. 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 for the first time. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were serving as inputs for 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. Notably, the meaningful MHWCS do not necessarily represent the most extreme values for individual hazards but instead maximize hazard intensities relative to the ensemble mean, collectively leading to significant disaster impacts. Our findings further underscore the importance of evaluating Pareto-optimal solutions to assist risk managers in understanding how combinations of TC meteorological variables—such as track, translation speed, intensity, size, and rainfall—shape worst-case scenarios. For instance, meaningful MHWCS forecasts tend to exhibit moderate meteorological characteristics comparable to the ensemble mean, with variability in translation speed emerging as the strongest single predictor of single-hazard worst-case scenarios.