Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations

• Published in: Journal of geophysical research. Machine learning and computation Vol. 2; no. 2
• Main Authors: Hiron, Luna, Zavala‐Romero, Olmo, Chassignet, Eric P., Miron, Philippe, Subrahmanyam, Bulusu
• Format: Journal Article
• Language: English
• Published: Wiley 01.06.2025
• Subjects: chlorophyll concentration; forecasting eddies; Lagrangian coherent eddies; Loop Current Eddies; machine learning; satellite observations
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2025JH000620

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. Plain Language Summary Lagrangian coherent eddies are types of vortices in the ocean that trap water in their interior and transport it without exchange with the exterior water. These eddies play a key role in transporting water properties such as heat and salt, as well as tracers such as oil, larvae, and seaweed (e.g., Sargassum) across the ocean. For example, during the 2010 Deepwater Horizon oil spill, a type of eddy called a Loop Current Frontal Eddy (LCFE) trapped some of the oil, keeping it from reaching the Florida Keys. This study uses machine learning and satellite data—sea surface height, sea surface temperature, and chlorophyll concentration—to identify and predict the lifetimes of Lagrangian coherent eddies in the GoM. Three durations of eddy coherence (5, 10, and 20 days) are analyzed. The machine learning model trained with sea surface height successfully identifies and predicts the lifetimes of eddies, achieving accuracy rates of 90% for Loop Current Eddies identification and 93% for lifetime prediction, along with 71% and 61% for LCFEs, respectively. Adding chlorophyll data from satellites improved the predictions compared to using sea surface height and temperature alone. This machine learning framework can advance predictions of eddy lifetimes and tracer transport. Key Points Machine learning can successfully identify and predict Lagrangian coherent eddies in the Gulf of Mexico The machine learning model achieved accuracy rates of 90% for identification and 93% for lifetime prediction of Loop Current Eddies Incorporating chlorophyll data enhances the machine learning model's ability to predict the Lagrangian coherence of eddies