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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025JH000620 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839653611469012992 |
|---|---|
| author | Luna Hiron Olmo Zavala‐Romero Eric P. Chassignet Philippe Miron Bulusu Subrahmanyam |
| author_facet | Luna Hiron Olmo Zavala‐Romero Eric P. Chassignet Philippe Miron Bulusu Subrahmanyam |
| author_sort | Luna Hiron |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1eb57a1afc1e4249bb3adb731bf42ea9 |
| institution | Matheson Library |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-1eb57a1afc1e4249bb3adb731bf42ea92025-06-25T14:02:49ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000620Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite ObservationsLuna Hiron0Olmo Zavala‐Romero1Eric P. Chassignet2Philippe Miron3Bulusu Subrahmanyam4Center for Ocean‐Atmospheric Prediction Studies Florida State University Tallahassee FL USACenter for Ocean‐Atmospheric Prediction Studies Florida State University Tallahassee FL USACenter for Ocean‐Atmospheric Prediction Studies Florida State University Tallahassee FL USACenter for Ocean‐Atmospheric Prediction Studies Florida State University Tallahassee FL USASchool of the Earth, Ocean, and Environment University of South Carolina Columbia SC USAAbstract 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.https://doi.org/10.1029/2025JH000620Lagrangian coherent eddiessatellite observationsmachine learningchlorophyll concentrationforecasting eddiesLoop Current Eddies |
| spellingShingle | Luna Hiron Olmo Zavala‐Romero Eric P. Chassignet Philippe Miron Bulusu Subrahmanyam Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations Journal of Geophysical Research: Machine Learning and Computation Lagrangian coherent eddies satellite observations machine learning chlorophyll concentration forecasting eddies Loop Current Eddies |
| title | Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations |
| title_full | Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations |
| title_fullStr | Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations |
| title_full_unstemmed | Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations |
| title_short | Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations |
| title_sort | identifying and predicting the lagrangian coherence of eddies in the gulf of mexico using machine learning and satellite observations |
| topic | Lagrangian coherent eddies satellite observations machine learning chlorophyll concentration forecasting eddies Loop Current Eddies |
| url | https://doi.org/10.1029/2025JH000620 |
| work_keys_str_mv | AT lunahiron identifyingandpredictingthelagrangiancoherenceofeddiesinthegulfofmexicousingmachinelearningandsatelliteobservations AT olmozavalaromero identifyingandpredictingthelagrangiancoherenceofeddiesinthegulfofmexicousingmachinelearningandsatelliteobservations AT ericpchassignet identifyingandpredictingthelagrangiancoherenceofeddiesinthegulfofmexicousingmachinelearningandsatelliteobservations AT philippemiron identifyingandpredictingthelagrangiancoherenceofeddiesinthegulfofmexicousingmachinelearningandsatelliteobservations AT bulususubrahmanyam identifyingandpredictingthelagrangiancoherenceofeddiesinthegulfofmexicousingmachinelearningandsatelliteobservations |