Medium-Term Forecasting of Loop Current Eddy Cameron and Eddy Darwin Formation in the Gulf of Mexico With a Divide-and-Conquer Machine Learning Approach

摘要

The Loop Current (LC) is the dominant circulation system in the Gulf of Mexico. A long-term prediction of the LC system (LCS) behavior is critical for understanding the Gulf of Mexico oceanography and ecosystem, and for mitigating outcomes of anthropogenic and natural disasters. In early 2018, the National Academies of Science, Engineering, and Medicine posed a challenge to the research community to develop systems that can forecast the movement of the LCS over longer periods of time than the current state of art. In this paper, a Recurrent Neural Network, the Long Short-Term Memory (LSTM) network, is applied to predict the LC evolution and the LC ring formation. The LSTM model is trained to learn patterns hidden in sea surface height (SSH) time series. To reduce the memory demand owing to the use of high spatial resolution SSH data set, the region of interest is partitioned into nonoverlapping subregions. After partitioning, an LSTM network is trained to predict the SSH in each subregion. A smoothing function is then applied to reduce discontinuities of the SSH predictions across the partition boundaries, hence error propagation. It is shown that such a machine learning model is capable of predicting the LCS SSH evolution 9 weeks in advance within 40 km in terms of the LCS frontal distance errors. Furthermore, it is shown that the model predicted the timing and general location of eddy Darwin's shedding event 12 weeks in advance, and eddy Cameron's detachment and reattachment 8 weeks in advance.