A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence-Reference-Cited by-同舟云学术

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

Published:2021-01 Issue:1 Volume:13 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Manucharyan Georgy E.¹^ORCID,Siegelman Lia²^ORCID,Klein Patrice²³⁴^ORCID

Affiliation:

1. School of Oceanography University of Washington Seattle WA USA

2. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

3. Laboratoire de Météorologie Dynamique Ecole Normale Supérieure CNRS Paris France

4. Laboratoire d'Oceanographie Physique et Spatiale IFREMER CNRS Brest France

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001965

Reference44 articles.

1. Global surface eddy diffusivities derived from satellite altimetry

2. Mapping the Energy Cascade in the North Atlantic Ocean: The Coarse-Graining Approach

3. Nonlinear Cascades of Surface Oceanic Geostrophic Kinetic Energy in the Frequency Domain*

4. On the resolutions of ocean altimetry maps

5. The dynamics of an equivalent-barotropic model of the wind-driven circulation

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