Research Group

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Loop Current System SSH and Subsurface Current Prediction with a Transfer Learning Approach

National Academies of Science, Engineering, Medicine: Gulf Research Program

Understanding Gulf Ocean Systems Grants 2

Using 18 years of sea surface height (SSH) fields and sub-surface observations, this project will apply machine learning tools to predict Loop Current speed, vertical structure, and duration, to examine the eddy formation and shedding processes. The project aims to improve predictive capability of the location and duration of the Loop Current over a forecast period of one month; eddy shedding at two-three months; and predictive skill for Loop Current and Loop Current Eddy speed, vertical structure, and duration over a several day forecast.

Dataset Available

Forecasting 3D velocity structures of the Loop Current system in the Gulf of Mexico using a deep learning model

Authors: Muhamed Ali, Ali
Published On: Jan 09 2024 22:00 UTC
File Format: nc
DOI: 10.7266/KX3MBKQR
UDI: U2.x931.000:0001
File Size: 1.27 GB

Laurent Cherubin
Associate Research Professor
Florida Atlantic University

lcherubin@fau.edu

Ali Muhamed Ali
Post Doctorate Researcher
Florida Atlantic University

amuhamedali2014@fau.edu

Muhamed Ali, A., Zhuang, H., VanZwieten, J., Ibrahim, A. K., & Chérubin, L. (2021). A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico. Forecasting, 3(4), 934–953. https://doi.org/10.3390/forecast3040056