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Steve Cherry

Abstract

The singular value decomposition analysis (SVD) method is discussed in the context of the simultaneous orthogonal rotation of two matrices. It is demonstrated that the singular vectors are rotated EOFs and the SVD expansion coefficients are rotated sets of principal component expansion coefficients. This way of thinking about SVD aids in the interpretation of results and provides guidance as to when and how to use SVD.

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Steve Cherry

Abstract

The goal of singular value decomposition analysis (SVD) and canonical correlation analysis (CCA) is to isolate important coupled modes between two geophysical fields of interest. In this paper the relationship between SVD and CCA is clarified. They should be considered as two distinct methods (possibly) suitable for answering two different questions. Some problems associated with interpreting results of both SVD and CCA are discussed. Both methods have a high potential to produce spurious spatial patterns. Caution is always called for in interpreting results from either method.

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