Orthogonal rotation of spatial patterns derived from singular value decomposition analysis

Abstract

Singular value decomposition (SVD) analysis is frequently used to identify pairs of spatial patterns whose time series are characterized by maximum temporal covariance. This paper introduces a method, an extension of SVD analysis, which linearly transforms a subset of total singular vectors into a set of alternative solutions using a varimax rotation. The linear transformation (known as 'rotation'), weighting singular vectors by the square roots of the corresponding singular values, emphasizes geographical regions characterized by the strongest relationships between two fields, so that spatial patterns corresponding to rotated singular vectors are more spatially localized. Several examples are shown to illustrate the effectiveness of the rotation in isolating coupled modes of variability inherent in meteorological datasets.