A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence

A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence

Abstract Satellite altimeters provide global observations of sea surface height (SSH) and present a unique data set for advancing our theoretical understanding of upper‐ocean dynamics and monitoring its variability. Considering that mesoscale SSH patterns can evolve on timescales comparable to or sh...

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Bibliographic Details
Main Authors: Georgy E. Manucharyan, Lia Siegelman, Patrice Klein
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-01-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
baroclinic instability
Deep Learning
deep ocean flows
mesoscale eddies
sea surface height interpolation
state estimation
Online Access:https://doi.org/10.1029/2019MS001965

Internet

https://doi.org/10.1029/2019MS001965

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