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 surge forecast models with respect to real-world water height
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 post factum. We trained our
proposed ML model on a dataset of 61 historical storms in the coastal regions
of the 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 all gauge station coordinates used for the initial data.
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 produced simulation accuracy. The presented work is an important
first step in creating a bias correction system for real-time storm surge
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 surge
forecasting.

利用 LSTM 深度学习网络模型对风暴潮预测模型中系统误差进行预测和修正，能够提高预测准确性。