Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics

Autor: Scott A. Martin, Georgy E. Manucharyan, Patrice Klein
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geophysical Research Letters, Vol 51, Iss 17, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2024GL110059
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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