Reconstructing the Subsurface Temperature Field by Using Sea Surface Data Through Self-Organizing Map Method

Cheng Chen; Kunde Yang; Yuanliang Ma; Yang Wang

doi:10.1109/LGRS.2018.2866237

Reconstructing the Subsurface Temperature Field by Using Sea Surface Data Through Self-Organizing Map Method

Cheng Chen, Kunde Yang, Yuanliang Ma, Yang Wang

Ocean Institute

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

Self-organizing map (SOM) method combined with the empirical orthogonal function was used to reconstruct the subsurface temperature field by using sea surface data in the Northwestern Pacific Ocean. In contrast to the traditional method, SOM method can extract nonlinear relations from the data and is more suitable for nonlinear dynamics in the ocean. Error statistics show that SOM method provides reconstructions of the subsurface temperature field with the majority of relative errors below 20% at 0-1000-m depth.

Original language	English
Article number	8458147
Pages (from-to)	1812-1816
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	15
Issue number	12
DOIs	https://doi.org/10.1109/LGRS.2018.2866237
State	Published - Dec 2018

Keywords

Empirical orthogonal function (EOF)
sea surface data
self-organizing map (SOM)
subsurface temperature reconstruction

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10.1109/LGRS.2018.2866237

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title = "Reconstructing the Subsurface Temperature Field by Using Sea Surface Data Through Self-Organizing Map Method",

abstract = "Self-organizing map (SOM) method combined with the empirical orthogonal function was used to reconstruct the subsurface temperature field by using sea surface data in the Northwestern Pacific Ocean. In contrast to the traditional method, SOM method can extract nonlinear relations from the data and is more suitable for nonlinear dynamics in the ocean. Error statistics show that SOM method provides reconstructions of the subsurface temperature field with the majority of relative errors below 20% at 0-1000-m depth.",

keywords = "Empirical orthogonal function (EOF), sea surface data, self-organizing map (SOM), subsurface temperature reconstruction",

author = "Cheng Chen and Kunde Yang and Yuanliang Ma and Yang Wang",

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AU - Ma, Yuanliang

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Y1 - 2018/12

N2 - Self-organizing map (SOM) method combined with the empirical orthogonal function was used to reconstruct the subsurface temperature field by using sea surface data in the Northwestern Pacific Ocean. In contrast to the traditional method, SOM method can extract nonlinear relations from the data and is more suitable for nonlinear dynamics in the ocean. Error statistics show that SOM method provides reconstructions of the subsurface temperature field with the majority of relative errors below 20% at 0-1000-m depth.

AB - Self-organizing map (SOM) method combined with the empirical orthogonal function was used to reconstruct the subsurface temperature field by using sea surface data in the Northwestern Pacific Ocean. In contrast to the traditional method, SOM method can extract nonlinear relations from the data and is more suitable for nonlinear dynamics in the ocean. Error statistics show that SOM method provides reconstructions of the subsurface temperature field with the majority of relative errors below 20% at 0-1000-m depth.

KW - Empirical orthogonal function (EOF)

KW - sea surface data

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KW - subsurface temperature reconstruction

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Reconstructing the Subsurface Temperature Field by Using Sea Surface Data Through Self-Organizing Map Method

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