Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

doi:10.1007/s00343-010-9074-6

IOCAS-IR > 海洋环流与波动重点实验室

	Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea
	Chen Haiying 1; Yin Baoshu 1; Fang Guohong 2; Wang Yonggang 2
	2010-09-01
发表期刊	CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY
ISSN	0254-4059
卷号	28 期号:5 页码:981-989
文章类型	Article
摘要	We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.; We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.
关键词	South China Sea Nonlinear Pca Satellite Data Inter-annual Variation
学科领域	Limnology ; Oceanography
DOI	10.1007/s00343-010-9074-6
URL	查看原文
收录类别	SCI
语种	英语
WOS记录号	WOS:000281711600005
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/5272
专题	海洋环流与波动重点实验室
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China 2.SOA, Inst Oceanog 1, Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China
第一作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Chen Haiying,Yin Baoshu,Fang Guohong,et al. Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea[J]. CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2010,28(5):981-989.
APA	Chen Haiying,Yin Baoshu,Fang Guohong,&Wang Yonggang.(2010).Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea.CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY,28(5),981-989.
MLA	Chen Haiying,et al."Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea".CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY 28.5(2010):981-989.

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