SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis
Abstract
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.
Related
- Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
- A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS
- A Generative AI Framework for Structural Analysis and DCGAN-Based Synthesis of Traditional Chinese Papercutting Patterns
- Generative SDEs with Trainable Diffusion and Density-Based Losses
- Scentree: a framework for generating scenario trees for multistage stochastic programming
BibTeX
@misc{zheng2026surgegen,
title = {{SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis}},
author = {Shunan Zheng and John J. Hasenbein},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03382},
eprint = {2609.03382},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03382},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis
AU - Shunan Zheng
AU - John J. Hasenbein
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.03382
UR - https://arxiv.org/abs/2609.03382
AB - 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.
ER -