ORTHOGONAL ROTATION OF SPATIAL PATTERNS DERIVED FROM SINGULAR-VALUE DECOMPOSITION ANALYSIS

Singular value decomposition (SVD) analysis is frequently used to iden tify pairs of spatial patterns whose time series are characterized by maximum temporal covariance. It tends to compress complicated temporal covariance between two fields into a relatively few pairs of spatial patterns by maximizing temporal covariance explained by each pair of s patial patterns white constraining them to be spatially orthogonal to the preceding ones of the same field. The resulting singular vectors a re sometimes complicated and difficult to interpret physically. This p aper introduces a method, an extension of SVD analysis, which linearly transforms a subset of total singular vectors into a set of alternati ve solutions using a varimax rotation. The linear transformation (know n as ''rotation''), weighting singular vectors by the square roots of the corresponding singular values, emphasizes geographical regions cha racterized by the strongest relationships between two fields, so that spatial patterns corresponding to rotated singular vectors are more sp atially localized. Several examples are shown to illustrate the effect iveness of the rotation in isolating coupled modes of variability inhe rent in meteorological datasets.