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

CORC > 海洋研究所 > 中国科学院海洋研究所 > 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
刊名	CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY
	2010-09-01
卷号	28 期号:5 页码:981-989
关键词	South China Sea Nonlinear Pca Satellite Data Inter-annual Variation
ISSN号	0254-4059
DOI	10.1007/s00343-010-9074-6
文献子类	Article
英文摘要	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.
学科主题	Limnology ; Oceanography
URL标识	查看原文
语种	英语
WOS记录号	WOS:000281711600005
公开日期	2010-12-24
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/5273]
专题	海洋研究所_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
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.