Abstract Research performed to-date on data assimilation (DA) in storm surge modeling has found it to have limited value for predicting rapid surge responses (e.g., those accompanying tropical cyclones). In this paper, we submit that a well-resolved, barotropic hydrodynamic model is typically able to capture the surge event itself, leaving slower processes that determine the large scale, background water level as primary sources of water level error. These “unresolved drivers” reflect physical processes not included in the model’s governing equations or forcing terms, such as far field atmospheric forcing, baroclinic processes, major ocean currents, steric variations, or precipitation. We have developed a novel, efficient, optimal interpolation-based DA scheme, using observations from coastal water level gages, that dynamically corrects for the presence of unresolved drivers. The methodology is applied for Hurricane Matthew (2016) and results demonstrate it is highly effective at removing water level residuals, roughly halving overall surge errors for that storm. The method is computationally efficient, well-suited for either hindcast or forecast applications and extensible to more advanced techniques and datasets.

中文翻译：

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使用数据同化对风暴潮模型进行低频水位修正

摘要 迄今为止对风暴潮模型中的数据同化（DA）进行的研究发现，它对于预测快速风暴响应（例如伴随热带气旋的响应）的价值有限。在本文中，我们提出，一个分辨率良好的正压水动力模型通常能够捕获涌浪事件本身，从而使确定大范围背景水位的较慢过程成为水位误差的主要来源。这些“未解决的驱动因素”反映了模型控制方程或强迫项中未包含的物理过程，例如远场大气强迫、斜压过程、主要洋流、空间变化或降水。我们开发了一种新颖、高效、基于最优插值的 DA 方案，利用沿海水位计的观测结果，动态纠正未解决的驱动因素的存在。该方法适用于飓风马修（2016 年），结果表明它在消除水位残留方面非常有效，大约将该风暴的总体浪涌误差减少了一半。该方法计算效率高，非常适合事后或预测应用，并且可扩展到更先进的技术和数据集。