Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network

A self-organizing map (SOM) neural network was developed from Argo gridded datasets in order to estimate a subsurface temperature anomaly (STA) from remote sensing data. The SOM maps were trained using anomalies of sea surface temperature (SST), height (SSH), and salinity (SSS) data from Argo gridde...

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Bibliographic Details
Published in:Journal of atmospheric and oceanic technology 2012-11, Vol.29 (11), p.1675-1688
Main Authors: Wu, Xiangbai, Yan, Xiao-Hai, Jo, Young-Heon, Liu, WTimothy
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Atmospherics
Estimates
Marine
Neural networks
Ocean temperature
Remote sensing
Salinity
Time series
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Summary:A self-organizing map (SOM) neural network was developed from Argo gridded datasets in order to estimate a subsurface temperature anomaly (STA) from remote sensing data. The SOM maps were trained using anomalies of sea surface temperature (SST), height (SSH), and salinity (SSS) data from Argo gridded monthly anomaly datasets, labeled with Argo STA data from 2005 through 2010, which were then used to estimate the STAs at different depths in the North Atlantic from the sea surface data. The estimated STA maps and time series were compared with Argo STAs including independent datasets for validation. In the Gulf Stream path areas, the STA estimations from the SOM algorithm show good agreement with in situ measurements taken from the surface down to 700-m depth, with a correlation coefficient larger than 0.8. Sensitivity of the SOM, when including salinity, shows that with SSS anomaly data in the SOM training process reveal the importance of SSS information, which can improve the estimation of STA in the subtropical ocean by up to 30%. In subpolar basins, the monthly climatology SST and SSH can also help to improve the estimation by as much as 40%. The STA time series for 1993-2004 in the midlatitude North Atlantic were estimated from remote sensing SST and altimetry time series using the SOM algorithm. Limitations for the SOM algorithm and possible error sources in the estimation are briefly discussed.
ISSN:0739-0572
1520-0426
DOI:10.1175/JTECH-D-12-00013.1