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A Guide to Empirical Orthogonal Functions for Climate Data AnalysisThe Canonical Correlation Analysis

A Guide to Empirical Orthogonal Functions for Climate Data Analysis: The Canonical Correlation... [The method based on the Singular Value Decomposition described in previous chapters was able to represent the largest amount of cross-covariance with fewest modes. The computed modes are designed so as to specifically explain most of the spatial variance. The spatial view point is a requirement that can be put on the modes, but it is hardly unique. Another commonly employed point of view arises if one considers the temporal variations of the modes. In this case one is interested in modes that generate maximally correlated time coefficients. This method has been developed in multi-variate analysis and it is known as “Canonical Correlation Analysis”] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guide to Empirical Orthogonal Functions for Climate Data AnalysisThe Canonical Correlation Analysis

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Publisher
Springer Netherlands
Copyright
© Springer Science+Business Media B.V. 2010
ISBN
978-90-481-3701-5
Pages
107 –121
DOI
10.1007/978-90-481-3702-2_7
Publisher site
See Chapter on Publisher Site

Abstract

[The method based on the Singular Value Decomposition described in previous chapters was able to represent the largest amount of cross-covariance with fewest modes. The computed modes are designed so as to specifically explain most of the spatial variance. The spatial view point is a requirement that can be put on the modes, but it is hardly unique. Another commonly employed point of view arises if one considers the temporal variations of the modes. In this case one is interested in modes that generate maximally correlated time coefficients. This method has been developed in multi-variate analysis and it is known as “Canonical Correlation Analysis”]

Published: Nov 27, 2009

Keywords: Weight Vector; Canonical Correlation Analysis; Height Field; Time Coefficient; Pacific North American Pattern

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