Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics - INSU - Institut national des sciences de l'Univers
Article Dans Une Revue Geophysical Research Letters Année : 2024

Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics

Résumé

Ocean eddies affect large-scale circulation and induce a kinetic energy cascade through their non-linear interactions. However, since global observations of eddy dynamics come from satellite altimetry maps that smooth eddies and distort their geometry, the strength of this cascade is underestimated. Here, we use deep learning to improve observational estimates of global surface geostrophic currents and explore the implications for the cascade. By synthesizing multi-modal satellite observations of sea surface height (SSH) and temperature, we achieve up to a 30% improvement in spatial resolution over the community-standard SSH product. This reveals numerous strongly interacting eddies that were previously obscured by smoothing. In many regions, these newly resolved eddies lead to nearly an order-of-magnitude increase in the upscale kinetic energy cascade that peaks in spring and is strong enough to drive the seasonality of large mesoscale eddies. Our study suggests that deep learning can be a powerful paradigm for satellite oceanography.
Fichier principal
Vignette du fichier
Geophysical Research Letters - 2024 - Martin - Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics.pdf (4.93 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

insu-04729834 , version 1 (10-10-2024)

Licence

Identifiants

Citer

Scott A. Martin, Georgy E. Manucharyan, Patrice Klein. Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 2024, 51, ⟨10.1029/2024GL110059⟩. ⟨insu-04729834⟩
30 Consultations
5 Téléchargements

Altmetric

Partager

More