Storm surge is a significant threat to coastal communities across the globe, responsible for loss of life and enormous property damage. Consequently, significant efforts have been expended to develop high-fidelity physics-based models for storm surge prediction. However, such models are often extremely computationally expensive and require supercomputing resources. In recent years, there has been a growing trend towards data-driven surrogate models, which approximate the capabilities of high-fidelity models at a tiny fraction of the computational cost. Most datasets of high-fidelity storm surge model output are limited to narrow geographical regions, with the majority focused on the continental United States and China. This trend is reflected in the scope of existing storm surge surrogate models. In this work, we present a novel dataset for training storm surge surrogate models with global applicability. The dataset consists of high-resolution peak surge output from the ADvanced CIRCulation (ADCIRC) model for over 15,000 landfalling synthetic storms distributed across the world. To the author's knowledge, it is the largest dataset of its kind ever assembled, and is unique in its global scope. We additionally present a machine learning model for peak storm surge based on computer vision architecture. The model is trained on our new global dataset and can accurately predict maximum storm surge in disparate geographical regions - including those for which few or no surrogate models exist. Both the dataset and accompanying model are publicly available, with the aim to support the development of additional storm surge models with global reach.

翻译：风暴潮是全球沿海社区面临的重大威胁，造成人员伤亡和巨大财产损失。为此，人们投入大量精力开发基于物理原理的高保真风暴潮预测模型。然而，这类模型通常计算成本极高，需要超级计算资源支持。近年来，数据驱动替代模型（surrogate models）的发展趋势日益显著，这类模型能以极低计算成本近似实现高保真模型的功能。现有高保真风暴潮模型输出数据集大多局限于狭小地理区域，且主要集中于美国本土和中国。这一趋势也反映在现有风暴潮替代模型的研究范围中。本研究提出一个具有全球适用性的新型数据集，用于训练风暴潮替代模型。该数据集包含来自先进环流模型（ADCIRC，Advanced CIRCulation model）的超过15000个全球登陆合成风暴的高分辨率峰值涌浪输出。据作者所知，这是同类数据集中规模最大的数据集，其全球覆盖范围具有独特性。我们同时提出基于计算机视觉架构的峰值风暴潮机器学习模型。该模型基于新开发的全球数据集进行训练，能准确预测不同地理区域的峰值风暴潮——包括那些几乎没有或完全没有替代模型的区域。数据集和配套模型均已公开，旨在支持全球范围更多风暴潮模型的开发。