发表机构
University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对传统风暴潮数值模拟计算成本高的问题，提出SurgeGen混合生成扩散框架，结合基线预测与条件扩散模型，可生成真实多样的风暴潮场景，适用于训练分布内外的条件。
AI 中文摘要
预测登陆热带气旋引发的风暴潮对于防洪减灾和海岸风险管理至关重要。传统基于物理的数值模型通过数值方法求解纳维-斯托克斯方程来模拟风暴潮，但这类模拟计算成本高昂。生成模型有望用于风暴潮仿真，因为它们能生成多样的实现结果，而非仅产生单一确定性预测，不过其在风暴潮仿真中的应用仍未得到充分探索。本文利用扩散模型构建风暴潮代理模型，结合基线预测阶段与条件生成，以提供更具可解释性的建模框架。我们开发了SurgeGen，这是一个两阶段生成框架，用于生成以连续空间中参数定义的假设风暴为条件的风暴潮场景。首先，基线模型生成风暴潮高度的粗略估计，该估计随后作为条件输入到扩散模型中，由扩散模型生成能更好捕捉空间模式和变异性的精细风暴潮场景。我们证明，所提方法能在训练分布内部及外部的条件下生成真实且多样的风暴潮场景。
英文摘要
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.
