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

Abstract

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.

A Deep Learning Model for Forecasting Sea Surface Height Anomalies and Temperatures in the South China Sea

“…One problem that deep learning systems seek to solve is the preservation of previous information for future prediction tasks (J. L. Wang et al., 2019). In this study, the Conv1D‐LSTM model, which combines a convolutional neural network (CNN) and a long short‐term memory (LSTM) network, serves as a time series prediction model (Shi et al., 2015).…”

Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model

“…In an effort to address this challenge, a deep learning approach to forecast the sea surface height (SSH) of the LC system was proposed in [3,4]. The problem was formulated as time sequence regression and prediction [5], and solved with a Recurrent Neural Network (RNN).…”

“…After each prediction, these principle component time series were reconstructed into the original subregions, and the partitions were pieced back together to produce an overall prediction of the region's SSH. With this Divide and Conquer (DAC) method, Wang et al [4] predicted the LC evolution and eddy shedding more than two and three months in advance, respectively. At every prediction step, the predicted SSH across the neighboring partitions were smoothed using an interpolation function described in [4].…”

Deep Learning for Geophysics: Current and Future Trends

“…DL can predict the loop current in the ocean by learning the pattern in sea surface height (SSH). An LSTM was proposed to predict SSH and current loop in the Gulf of Mexico within 40 kilometers nine weeks in advance (Wang, Zhuang, et al, 2019). Due to the limit of computational memory, the region of interest is split into different sub-regions.…”