14th Conference on Applied Climatology
15th Symposium on Global Change and Climate Variations

JP5.4

Nonlinear Complex Principal Component Analysis, with Applications to Tropical Pacific Wind Variability

Sanjay S.P. Rattan, University of British Columbia, Vancouver, BC, Canada; and W. W. Hsieh

Nonlinear complex principal component analysis (NLCPCA) can be performed by complex-valued neural network models to extract both linear and nonlinear relations between variables in 2-D vector fields such as horizontal winds or currents. The NLCPCA is a generalization of the complex principal component analysis (CPCA) which determines only linear relationships between complex variables. The NLCPCA is a complex-valued neural network algorithm for data dimensionality reduction and feature extraction in the complex domain. An important distinction is drawn between 2-D NLPCA, that is for dimensionality reduction in the real domain to two real variables, and the NLCPCA which reduces the dimension of several complex variables to a single complex variable. A theory of NLCPCA is developed and applied to analyze the tropical Pacific wind field. The linear CPCA of the tropical Pacific wind field describes the El Niño - La Niña  phenomenon in about four modes, whereas the NLCPCA model extracts the full 2-D phenomenon in a single nonlinear mode.

Joint Poster Session 5, Climate Variability (JOINT with THE 15TH SYMPOSIUM ON GLOBAL CHANGE AND CLIMATE VARIATIONS AND THE 14TH CONFERENCE ON APPLIED CLIMATOLOGY; Hall 4AB)
Wednesday, 14 January 2004, 2:30 PM-4:00 PM, Hall 4AB

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