Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSII), thus allowing for a better characterization of the mesoscale and submesoscale eddy field. However, to fulfill the promises of...
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fttriple:oai:gotriple.eu:10670/1.ane0we 2023-05-15T17:32:40+02:00 Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks Lguensat, Redouane Fablet, Ronan Le Sommer, Julien Metref, Sammy Cosme, Emmanuel Ouenniche, Kaouther Drumetz, Lucas Gula, Jonathan 2020-01-01 https://doi.org/10.1109/IGARSS39084.2020.9323531 https://archimer.ifremer.fr/doc/00719/83102/88335.pdf https://archimer.ifremer.fr/doc/00719/83102/ en eng IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 3904-3907 doi:10.1109/IGARSS39084.2020.9323531 10670/1.ane0we https://archimer.ifremer.fr/doc/00719/83102/88335.pdf https://archimer.ifremer.fr/doc/00719/83102/ other Archimer, archive institutionnelle de l'Ifremer geo envir Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2020 fttriple https://doi.org/10.1109/IGARSS39084.2020.9323531 2023-01-22T18:35:03Z The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSII), thus allowing for a better characterization of the mesoscale and submesoscale eddy field. However, to fulfill the promises of this mission, filtering the tidal component of the SSH measurements is necessary. This challenging problem is crucial since the posterior studies done by physical oceanographers using SWOT data will depend heavily on the selected filtering schemes. In this paper, we cast this problem into a supervised learning framework and propose the use of convolutional neural networks (ConvNets) to estimate fields free of internal tide signals. Numerical experiments based on an advanced North Atlantic simulation of the ocean circulation (eNATL60) show that our ConvNet considerably reduces the imprint of the internal waves in SSII data even in regions unseen by the neural network. We also investigate the relevance of considering additional data from other sea surface variables such as sea surface temperature (SST). Text North Atlantic Unknown IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium 3904 3907 |
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geo envir Lguensat, Redouane Fablet, Ronan Le Sommer, Julien Metref, Sammy Cosme, Emmanuel Ouenniche, Kaouther Drumetz, Lucas Gula, Jonathan Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
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The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSII), thus allowing for a better characterization of the mesoscale and submesoscale eddy field. However, to fulfill the promises of this mission, filtering the tidal component of the SSH measurements is necessary. This challenging problem is crucial since the posterior studies done by physical oceanographers using SWOT data will depend heavily on the selected filtering schemes. In this paper, we cast this problem into a supervised learning framework and propose the use of convolutional neural networks (ConvNets) to estimate fields free of internal tide signals. Numerical experiments based on an advanced North Atlantic simulation of the ocean circulation (eNATL60) show that our ConvNet considerably reduces the imprint of the internal waves in SSII data even in regions unseen by the neural network. We also investigate the relevance of considering additional data from other sea surface variables such as sea surface temperature (SST). |
format |
Text |
author |
Lguensat, Redouane Fablet, Ronan Le Sommer, Julien Metref, Sammy Cosme, Emmanuel Ouenniche, Kaouther Drumetz, Lucas Gula, Jonathan |
author_facet |
Lguensat, Redouane Fablet, Ronan Le Sommer, Julien Metref, Sammy Cosme, Emmanuel Ouenniche, Kaouther Drumetz, Lucas Gula, Jonathan |
author_sort |
Lguensat, Redouane |
title |
Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
title_short |
Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
title_full |
Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
title_fullStr |
Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
title_full_unstemmed |
Filtering Internal Tides from Wide-Swath Altimeter Data Using Convolutional Neural Networks |
title_sort |
filtering internal tides from wide-swath altimeter data using convolutional neural networks |
publisher |
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 3904-3907 |
publishDate |
2020 |
url |
https://doi.org/10.1109/IGARSS39084.2020.9323531 https://archimer.ifremer.fr/doc/00719/83102/88335.pdf https://archimer.ifremer.fr/doc/00719/83102/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Archimer, archive institutionnelle de l'Ifremer |
op_relation |
doi:10.1109/IGARSS39084.2020.9323531 10670/1.ane0we https://archimer.ifremer.fr/doc/00719/83102/88335.pdf https://archimer.ifremer.fr/doc/00719/83102/ |
op_rights |
other |
op_doi |
https://doi.org/10.1109/IGARSS39084.2020.9323531 |
container_title |
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium |
container_start_page |
3904 |
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3907 |
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