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

Eddy heat fluxes crucially affect large-scale oceanic currents but are challenging to monitor on a global scale. Here the authors develop a Deep Learning model to predict the eddy heat fluxes from sea surface height data only, bypassing the need for simultaneous observations of the deep ocean.

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tom M. George, Georgy E. Manucharyan, Andrew F. Thompson
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/c7651d6337f64a0bb9c40bf8010fa9bc
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

id	oai:doaj.org-article:c7651d6337f64a0bb9c40bf8010fa9bc
record_format	dspace
spelling	oai:doaj.org-article:c7651d6337f64a0bb9c40bf8010fa9bc2021-12-02T14:06:23ZDeep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence10.1038/s41467-020-20779-92041-1723https://doaj.org/article/c7651d6337f64a0bb9c40bf8010fa9bc2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20779-9https://doaj.org/toc/2041-1723Eddy heat fluxes crucially affect large-scale oceanic currents but are challenging to monitor on a global scale. Here the authors develop a Deep Learning model to predict the eddy heat fluxes from sea surface height data only, bypassing the need for simultaneous observations of the deep ocean.Tom M. GeorgeGeorgy E. ManucharyanAndrew F. ThompsonNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tom M. George Georgy E. Manucharyan Andrew F. Thompson Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
description	Eddy heat fluxes crucially affect large-scale oceanic currents but are challenging to monitor on a global scale. Here the authors develop a Deep Learning model to predict the eddy heat fluxes from sea surface height data only, bypassing the need for simultaneous observations of the deep ocean.
format	article
author	Tom M. George Georgy E. Manucharyan Andrew F. Thompson
author_facet	Tom M. George Georgy E. Manucharyan Andrew F. Thompson
author_sort	Tom M. George
title	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_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_sort	deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c7651d6337f64a0bb9c40bf8010fa9bc
work_keys_str_mv	AT tommgeorge deeplearningtoinfereddyheatfluxesfromseasurfaceheightpatternsofmesoscaleturbulence AT georgyemanucharyan deeplearningtoinfereddyheatfluxesfromseasurfaceheightpatternsofmesoscaleturbulence AT andrewfthompson deeplearningtoinfereddyheatfluxesfromseasurfaceheightpatternsofmesoscaleturbulence
_version_	1718392048257597440

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

Documents similaires