A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico

Ali Muhamed Ali, Hanqi Zhuang, James VanZwieten, Ali K. Ibrahim and Laurent Chérubin
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Ali Muhamed Ali: EECS Department, Florida Atlantic University, Boca Raton, FL 33431, USA
Hanqi Zhuang: EECS Department, Florida Atlantic University, Boca Raton, FL 33431, USA
James VanZwieten: EECS Department, Florida Atlantic University, Boca Raton, FL 33431, USA
Ali K. Ibrahim: Harbor Branch Oceanographic Institute, Florida Atlantic University, Boca Raton, FL 33431, USA
Laurent Chérubin: Harbor Branch Oceanographic Institute, Florida Atlantic University, Boca Raton, FL 33431, USA

Forecasting, 2021, vol. 3, issue 4, 1-20

Abstract: Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly in ocean regions that are characterized by rapid changes in their circulation due to changes in atmospheric forcing or due to the release of available potential energy through the development of instabilities. Ocean numerical models’ useful forecast window is no longer than two days over a given area with the best initialization possible. Predictions quickly diverge from the observational field throughout the water and become unreliable, despite the fact that they can simulate the observed dynamics through other variables such as temperature, salinity and sea surface height. Numerical methods such as harmonic analysis are used to predict both short- and long-term tidal currents with significant accuracy. However, they are limited to the areas where the tide was measured. In this study, a new approach to ocean current prediction based on deep learning is proposed. This method is evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than 4 days with a root mean square error less than 0.05 cm·s − 1 and a correlation coefficient of 0.6.

Keywords: deep learning; Loop Current; ocean current forecasting; LSTM; ocean measurements (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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