Nonlinear complex principal component analysis of the tropical Pacific interannual wind variability

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...

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Bibliographic Details
Published in:Geophysical research letters 2004-11, Vol.31 (21), p.L21201.1-n/a
Main Authors: Rattan, Sanjay S. P., Hsieh, William W.
Format: Article
Language:English
Subjects:
Earth sciences
Earth, ocean, space
Exact sciences and technology
Marine
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Summary: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.
ISSN:0094-8276
1944-8007
DOI:10.1029/2004GL020446