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This paper introduces SurgeGen, a hybrid generative diffusion framework designed to synthesize storm surge scenarios based on hypothetical tropical cyclone parameters. By combining a baseline prediction model with a diffusion model for conditional generation, SurgeGen effectively captures the spatial patterns and variability of storm surges while significantly reducing computational costs compared to traditional physics-based simulations. The results show that SurgeGen can produce realistic and diverse storm surge scenarios, even under conditions not seen during training, enhancing flood mitigation strategies and coastal risk management efforts.
SurgeGen can generate diverse and realistic storm surge scenarios, offering a computationally efficient alternative to traditional physics-based models.
Predicting storm surge induced by landfalling tropical cyclones is crucial for flood mitigation and coastal risk management. Traditionally, physics-based numerical models simulate storm surge by solving the Navier--Stokes equations using numerical methods, but these simulations are computationally expensive. Generative models are promising for storm surge emulation because they can generate diverse realizations rather than producing a single deterministic prediction. However, their use for storm surge emulation remains largely unexplored. In this paper, we leverage diffusion models for storm surge surrogate modeling, combining a baseline prediction stage with conditional generation to provide a more interpretable modeling framework. We develop SurgeGen, a two-stage generative framework for generating storm surge scenarios conditioned on hypothetical storms with parameters defined in a continuous space. First, a baseline model produces a coarse estimate of the storm surge height. This estimate then conditions a diffusion model, which generates refined storm surge scenarios that better capture spatial patterns and variability. We demonstrate that our approach can generate realistic and diverse storm surge scenarios under conditions both within and outside the training distribution.
