Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations
Abstract Lagrangian coherent eddies efficiently transport water properties, such as heat and salt, as well as tracers, including oil, larvae, and Sargassum, throughout the ocean. For instance, during the 2010 Deepwater Horizon oil spill, part of the oil was captured within a Loop Current Frontal Edd...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025JH000620 |
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| Summary: | Abstract Lagrangian coherent eddies efficiently transport water properties, such as heat and salt, as well as tracers, including oil, larvae, and Sargassum, throughout the ocean. For instance, during the 2010 Deepwater Horizon oil spill, part of the oil was captured within a Loop Current Frontal Eddy (LCFE), preventing it from reaching the Florida Keys. Similarly, Loop Current Eddies (LCEs) carry warmer, saltier waters typical of the Caribbean Sea to the western Gulf of Mexico (GoM). In this study, we employ machine learning alongside various satellite observations—absolute dynamic topography (ADT), sea surface temperature (SST), and chlorophyll‐a (Chl‐a)—to identify Lagrangian coherent eddies in the GoM and predict their lifetime. Three durations of Lagrangian coherence are investigated: 5, 10, and 20 days. This study also investigates the contributions of Chl‐a to identifying and forecasting LCEs' and LCFEs' Lagrangian coherence, aiming to assess the advantages of integrating this data set into data‐assimilative Gulf ocean models, in addition to ADT and SST. The machine learning model trained with ADT successfully identifies and predicts the lifetimes of eddies, achieving accuracy rates of 90% for LCE identification and 93% for lifetime prediction, along with 71% and 61% for LCFEs, respectively. Incorporating SST and Chl‐a enhanced eddy predictions over ADT‐only or ADT and SST combined, in particular LCEs and LCFEs, highlighting the benefits of assimilating Chl‐a into ocean models to improve the representation and the forecast of these eddies. This machine learning framework has the potential to advance predictions of eddy lifetimes and the advection of various tracers. |
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| ISSN: | 2993-5210 |