Publication Date:
2019
Description:
Abstract
The Loop Current (LC) is the dominant circulation system in the Gulf of Mexico (GoM). A long‐term prediction of the LC system (LCS) behavior is critical for understanding the GoM 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 dataset, the region of interest is partitioned into non‐overlapping sub‐regions. After partitioning, an LSTM network is trained to predict the SSH in each sub‐region. 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 nine weeks in advance within 40 kilometers 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 twelve weeks in advance, and eddy Cameron's detachment and re‐attachment eight weeks in advance.
Print ISSN:
2169-9275
Electronic ISSN:
2169-9291
Topics:
Geosciences
,
Physics
Permalink