State-space discrimination and clustering of atmospheric time series data based on Kullback information measures

Statistical problems in atmospheric science are frequently characterized by large spatio-temporal data sets and pose difficult challenges in classification and pattern recognition. Here, we consider the problem of identifying geographically homogeneous regions based on similarities in the temporal dynamics of weather patterns. Two disparity measures are proposed and applied to cluster time series of observed monthly temperatures from locations across Colorado, U.S.A. The two disparity measures are based on state-space models, where the monthly temperature anomaly dynamics and seasonal variation are represented by latent processes. Our disparity measures produce clusters consistent with known atmospheric flow structures. In particular, the temporal anomaly pattern is related to the topography of Colorado, where, separated by the Continental Divide, the flow structures in the western and eastern parts of the state have different dynamics. The results further suggest that seasonal variation may be affected by locally changing solar radiation levels primarily associated with elevation variations across the Rocky Mountains. The general methodology is outlined and developed in the Appendix. We conclude with a discussion of extensions to time varying and non-stationary systems. Copyright (c) 2007 John Wiley & Sons, Ltd.