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Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence

Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the global ocean circulation remains difficult as it requires simultaneous ob...

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Autores principales: George, Tom M., Manucharyan, Georgy E., Thompson, Andrew F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865057/
https://www.ncbi.nlm.nih.gov/pubmed/33547299
http://dx.doi.org/10.1038/s41467-020-20779-9
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author George, Tom M.
Manucharyan, Georgy E.
Thompson, Andrew F.
author_facet George, Tom M.
Manucharyan, Georgy E.
Thompson, Andrew F.
author_sort George, Tom M.
collection PubMed
description Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the global ocean circulation remains difficult as it requires simultaneous observations of upper-ocean velocity fields and interior temperature and density properties. Here we demonstrate that for quasigeostrophic baroclinic turbulence the eddy patterns in SSH snapshots alone contain sufficient information to estimate the eddy heat fluxes. We use simulations of baroclinic turbulence for the supervised learning of a deep Convolutional Neural Network (CNN) to predict up to 64% of eddy heat flux variance. CNNs also significantly outperform other conventional data-driven techniques. Our results suggest that deep CNNs could provide an effective pathway towards an operational monitoring of eddy heat fluxes using satellite altimetry and other remote sensing products.
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spelling pubmed-78650572021-02-11 Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence George, Tom M. Manucharyan, Georgy E. Thompson, Andrew F. Nat Commun Article Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the global ocean circulation remains difficult as it requires simultaneous observations of upper-ocean velocity fields and interior temperature and density properties. Here we demonstrate that for quasigeostrophic baroclinic turbulence the eddy patterns in SSH snapshots alone contain sufficient information to estimate the eddy heat fluxes. We use simulations of baroclinic turbulence for the supervised learning of a deep Convolutional Neural Network (CNN) to predict up to 64% of eddy heat flux variance. CNNs also significantly outperform other conventional data-driven techniques. Our results suggest that deep CNNs could provide an effective pathway towards an operational monitoring of eddy heat fluxes using satellite altimetry and other remote sensing products. Nature Publishing Group UK 2021-02-05 /pmc/articles/PMC7865057/ /pubmed/33547299 http://dx.doi.org/10.1038/s41467-020-20779-9 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
George, Tom M.
Manucharyan, Georgy E.
Thompson, Andrew F.
Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title_full Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title_fullStr Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title_full_unstemmed Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title_short Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
title_sort deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865057/
https://www.ncbi.nlm.nih.gov/pubmed/33547299
http://dx.doi.org/10.1038/s41467-020-20779-9
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