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

CORC > 厦门大学 > 海洋环境－已发表论文

	Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network
	Wu, Xiangbai ; Yan, Xiao-Hai ; Jo, Young-Heon ; Liu, W. Timothy ; Yan XH(严晓海)
刊名	http://dx.doi.org/10.1175/JTECH-D-12-00013.1
	2012-11
关键词	MIXED-LAYER DEPTH MULTIDECADAL OSCILLATION MEDITERRANEAN OUTFLOW THERMAL STRUCTURE ANALYTICAL MODEL SEA VARIABILITY WATER ARGO
英文摘要	NASA Physical Oceanography Program; NASA EPSCoR Program; NASA Space Grant; NOAA Sea Grant; 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 SUM, 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 SUM algorithm and possible error sources in the estimation are briefly discussed.
语种	英语
出版者	J ATMOS OCEAN TECH
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/87742]
专题	海洋环境－已发表论文
推荐引用方式 GB/T 7714	Wu, Xiangbai,Yan, Xiao-Hai,Jo, Young-Heon,et al. Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network[J]. http://dx.doi.org/10.1175/JTECH-D-12-00013.1,2012.
APA	Wu, Xiangbai,Yan, Xiao-Hai,Jo, Young-Heon,Liu, W. Timothy,&严晓海.(2012).Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network.http://dx.doi.org/10.1175/JTECH-D-12-00013.1.
MLA	Wu, Xiangbai,et al."Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network".http://dx.doi.org/10.1175/JTECH-D-12-00013.1 (2012).