FN Archimer Export Format PT J TI A Deep Learning approach to spatiotemporal SSH interpolation and estimation of deep currents in geostrophic ocean turbulence BT AF Manucharyan , Georgy E. Siegelman , Lia KLEIN, Patrice AS 1:1;2:2;3:2,3,4; FF 1:;2:;3:; C1 School of Oceanography, University of Washington, Seattle, WA, USA Jet Propulsion Laboratory, California Institute of Technology Pasadena CA, USA Laboratoire de Météorologie Dynamique, CNRS Ecole Normale Supérieure, Paris, France Laboratoire d'Oceanographie Physique et Spatiale, IFREMER,CNRS, Brest, France C2 UNIV WASHINGTON, USA JET PROP LAB, USA CNRS, FRANCE CNRS, FRANCE UM LOPS IN WOS Cotutelle UMR DOAJ copubli-int-hors-europe IF 8.469 TC 28 UR https://archimer.ifremer.fr/doc/00663/77502/79235.pdf LA English DT Article DE ;baroclinic instability;Deep Learning;deep ocean flows;mesoscale eddies;sea surface height interpolation;state estimation AB Satellite altimeters provide global observations of sea surface height (SSH) and present a unique dataset 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 shorter than satellite return periods, it is challenging to accurately reconstruct the continuous SSH evolution as currently available altimetry observations are still spatially and temporally sparse. Here we explore the possibility of SSH interpolation via Deep Learning by using synthetic observations from an idealized quasigeostrophic (QG) model of baroclinic ocean turbulence. We demonstrate that Convolutional Neural Networks with Residual Learning are superior in SSH reconstruction to linear and recently developed dynamical interpolation techniques. Also, the deep neural networks can provide a skillful state estimate of unobserved deep ocean currents at mesoscales. These conspicuous results suggest that SSH patterns of eddies might contain substantial information about the underlying deep ocean currents that are necessary for SSH prediction. Our training data is focused on highly idealized physics and diversification of processes needs to be considered to more accurately represent the real ocean. In addition, methodological improvements such as transfer learning and implementation of dynamically‐aware loss functions might be necessary to consider before its ultimate use with real satellite observations. Nonetheless, by providing a proof of concept based on synthetic data, our results point to deep learning as a viable alternative to existing interpolation and, more generally, state estimation methods for satellite observations of eddying currents. Plain Language Summary Satellite observations of sea surface height (SSH) are widely used to derive surface ocean currents on a global scale. However, due to gaps in SSH observations, it remains challenging to retrieve the dynamics of rapidly evolving upper‐ocean currents. To overcome this limitation, we propose a Deep Learning framework that is based on pattern recognition extracted from SSH observations. Using synthetic data generated from a simplified model of ocean turbulence, we demonstrate that deep learning can accurately estimate both surface and sub‐surface ocean currents, significantly outperforming the most commonly used techniques. By providing a proof of concept, our study highlights the strong potential of deep learning for estimating ocean currents from satellite observations. PY 2021 PD JAN SO Journal Of Advances In Modeling Earth Systems SN 1942-2466 PU American Geophysical Union (AGU) VL 13 IS 1 UT 000613327900002 DI 10.1029/2019MS001965 ID 77502 ER EF