The Use of a Statistical Forecast Criterion to Evaluate Alternative Empirical Spatial Oscillation Pattern Decomposition Methods in Climatological Fields

Spatial patterns of oscillation in climatological records aid the phenomenological understanding of atmospheric dynamics. The empirical identification of spatial oscillation patterns has been realized using a collection of techniques including empirical orthogonal functions (EOFs) canonical correlation analyses (CCAs) and first order Markov modeling via principal oscillation patterns (POPs). Each of these methods represent the motion of the field under study as a linear combination of a number of spatial patterns forced by its own characteristic temporal oscillation pattern. In practice it can be difficult to determine which of these alternative approaches is most appropriate for a given data set. Also for a given method the number of spatial patterns required to adequately capture the temporal and spatial motion of the field is often unclear. In this paper we consider the use of a statistical forecast criterion for resolving these issues. The approach is illustrated by applications of..