Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm tide forecast models with respect to real-world water elevation observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results (i.e., to correct the model bias). We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the south and southeastern U.S. and we tested its performance in bias correcting modeled water level data predictions from Hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for Hurricane Ian – unknown to the ML model – at the majority of gauge station coordinates. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the forecast accuracy. The presented work is an important first step in creating a bias correction system for real-time storm tide forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm tide forecasting.

中文翻译：

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AI时代的风暴潮建模：利用基于LSTM的机器学习提高预测精度

自然过程的物理模拟结果通常不能完全反映现实世界。例如，这是由于模拟的物理过程及其精确度的限制造成的。在这项工作中，我们提出并分析了基于 LSTM 的深度学习网络机器学习 (ML) 架构的使用，用于捕获和预测风暴潮预报模型相对于来自测量站的真实水位观测的系统误差行为在飓风事件期间。这项工作的总体目标是预测物理模型的系统误差并用它来提高模拟结果的准确性（即纠正模型偏差）。我们在美国南部和东南部沿海地区 61 场历史风暴的数据集上训练了我们提出的 ML 模型，并测试了其在修正伊恩飓风（2022 年）建模水位数据预测偏差方面的性能。我们表明，我们的模型可以持续提高大多数测量站坐标处的飓风伊恩（ML 模型未知）的预测准确性。此外，通过检查使用初始训练数据集的不同子集（包含许多在飓风轨迹方面相对相似或不同的飓风）的影响，我们发现仅使用六个飓风的子集就可以获得类似质量的偏差校正。这是一个重要的结果，意味着可以将预先训练的机器学习模型应用于实时飓风预报结果，以纠正偏差并提高预报精度。目前的工作是创建适用于全模拟区域的实时风暴潮预报偏差校正系统的重要第一步。它还提供了一种高度可移植且可操作应用的方法，用于提高风暴潮预报之外的各种物理模拟场景的准确性。