Derivation and Evaluation of Satellite-Based Surface Current

Observations of real-time ocean surface currents allow one to search and rescue at ocean disaster sites and investigate the surface transport and fate of ocean contaminants. Although real-time surface currents have been mapped by high-frequency (HF) radar, shipboard instruments, satellite altimetry,...

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Autores principales:	Jun Myoung Choi, Wonkook Kim, Tran Thy My Hong, Young-Gyu Park
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	surface current geostationary satellite convolutional neural network sea surface temperature particle tracking velocimetry submesoscale circulations Science Q General. Including nature conservation, geographical distribution QH1-199.5
Acceso en línea:	https://doaj.org/article/b8de39352f454a17b8eeba1322423a93
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spelling	oai:doaj.org-article:b8de39352f454a17b8eeba1322423a932021-11-15T04:36:46ZDerivation and Evaluation of Satellite-Based Surface Current2296-774510.3389/fmars.2021.695780https://doaj.org/article/b8de39352f454a17b8eeba1322423a932021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmars.2021.695780/fullhttps://doaj.org/toc/2296-7745Observations of real-time ocean surface currents allow one to search and rescue at ocean disaster sites and investigate the surface transport and fate of ocean contaminants. Although real-time surface currents have been mapped by high-frequency (HF) radar, shipboard instruments, satellite altimetry, and surface drifters, geostationary satellites have proved their capability in satisfying both basin-scale coverage and high spatiotemporal resolutions not offered by other observational platforms. In this paper, we suggest a strategy for the production of operational surface currents using geostationary satellite data, the particle image velocimetry (PIV) method, and deep learning-based evaluation. We used the model scalar field and its gradient to calculate the corresponding surface current via PIV, and we estimated the error between the true velocity field and calculated velocity field by the combined magnitude and relevance index (CMRI) error. We used the model datasets to train a convolutional neural network, which can be used to filter out bad vectors in the surface current produced by arbitrary model scalar fields. We also applied the pretrained network to the surface current generated from real-time Himawari-8 skin sea surface temperature (SST) data. The results showed that the deep learning network successfully filtered out bad vectors in a surface current when it was applied to model SST and created stronger dynamic features when the network was applied to Himawari SST. This strategy can help to provide a quality flag in satellite data to inform data users about the reliability of PIV-derived surface currents.Jun Myoung ChoiWonkook KimTran Thy My HongYoung-Gyu ParkFrontiers Media S.A.articlesurface currentgeostationary satelliteconvolutional neural networksea surface temperatureparticle tracking velocimetrysubmesoscale circulationsScienceQGeneral. Including nature conservation, geographical distributionQH1-199.5ENFrontiers in Marine Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface current geostationary satellite convolutional neural network sea surface temperature particle tracking velocimetry submesoscale circulations Science Q General. Including nature conservation, geographical distribution QH1-199.5
spellingShingle	surface current geostationary satellite convolutional neural network sea surface temperature particle tracking velocimetry submesoscale circulations Science Q General. Including nature conservation, geographical distribution QH1-199.5 Jun Myoung Choi Wonkook Kim Tran Thy My Hong Young-Gyu Park Derivation and Evaluation of Satellite-Based Surface Current
description	Observations of real-time ocean surface currents allow one to search and rescue at ocean disaster sites and investigate the surface transport and fate of ocean contaminants. Although real-time surface currents have been mapped by high-frequency (HF) radar, shipboard instruments, satellite altimetry, and surface drifters, geostationary satellites have proved their capability in satisfying both basin-scale coverage and high spatiotemporal resolutions not offered by other observational platforms. In this paper, we suggest a strategy for the production of operational surface currents using geostationary satellite data, the particle image velocimetry (PIV) method, and deep learning-based evaluation. We used the model scalar field and its gradient to calculate the corresponding surface current via PIV, and we estimated the error between the true velocity field and calculated velocity field by the combined magnitude and relevance index (CMRI) error. We used the model datasets to train a convolutional neural network, which can be used to filter out bad vectors in the surface current produced by arbitrary model scalar fields. We also applied the pretrained network to the surface current generated from real-time Himawari-8 skin sea surface temperature (SST) data. The results showed that the deep learning network successfully filtered out bad vectors in a surface current when it was applied to model SST and created stronger dynamic features when the network was applied to Himawari SST. This strategy can help to provide a quality flag in satellite data to inform data users about the reliability of PIV-derived surface currents.
format	article
author	Jun Myoung Choi Wonkook Kim Tran Thy My Hong Young-Gyu Park
author_facet	Jun Myoung Choi Wonkook Kim Tran Thy My Hong Young-Gyu Park
author_sort	Jun Myoung Choi
title	Derivation and Evaluation of Satellite-Based Surface Current
title_short	Derivation and Evaluation of Satellite-Based Surface Current
title_full	Derivation and Evaluation of Satellite-Based Surface Current
title_fullStr	Derivation and Evaluation of Satellite-Based Surface Current
title_full_unstemmed	Derivation and Evaluation of Satellite-Based Surface Current
title_sort	derivation and evaluation of satellite-based surface current
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b8de39352f454a17b8eeba1322423a93
work_keys_str_mv	AT junmyoungchoi derivationandevaluationofsatellitebasedsurfacecurrent AT wonkookkim derivationandevaluationofsatellitebasedsurfacecurrent AT tranthymyhong derivationandevaluationofsatellitebasedsurfacecurrent AT younggyupark derivationandevaluationofsatellitebasedsurfacecurrent
_version_	1718428827871346688

Derivation and Evaluation of Satellite-Based Surface Current

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