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Nonlinear complex principal component analysis of the tropical Pacific interannual wind variability
Author(s) -
Rattan Sanjay S. P.,
Hsieh William W.
Publication year - 2004
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2004gl020446
Subject(s) - principal component analysis , climatology , nonlinear system , multivariate statistics , mode (computer interface) , asymmetry , environmental science , divergence (linguistics) , atmospheric sciences , meteorology , geology , mathematics , statistics , geography , physics , computer science , quantum mechanics , operating system , linguistics , philosophy
Complex principal component analysis (CPCA) is a linear multivariate technique commonly applied to complex variables or 2‐dimensional vector fields such as winds or currents. A new nonlinear CPCA (NLCPCA) method has been developed via complex‐valued neural networks. NLCPCA is applied to the tropical Pacific wind field to study the interannual variability. Compared to the CPCA mode 1, the NLCPCA mode 1 is found to explain more variance and reveal the asymmetry in the wind anomalies between El Niño and La Niña states.