A Significance Test for Principal Components Applied to a Cyclone Climatology

Abstract

A technique is presented for selection of principal components for which the geophysical signal is greater than the level of noise. The level of noise is simulated by repeated sampling of principal components computed from a spatially and temporally uncorrected random process. By contrasting the application of principal components based upon the covariance matrix and correlation matrix for a given data set of cyclone frequencies, it is shown that the former is more suitable to fitting data and locating the individual variables that represent large variance in the record, while the latter is more suitable for resolving spatial oscillations such as the movement of primary storm tracks.