USE OF TEMPORAL PRINCIPAL COMPONENTS-ANALYSIS TO DETERMINE SEASONAL PERIODS

Temporal principal components analysis was applied separately to month ly long-term wind, temperature, and precipitation data for Southern Ca lifornia. Physical explanations of the significant eigenvectors are pr esented. Cluster analysis of the component loadings was then used to f orm groups of months (seasons) having similar spatial patterns. The re sulting groupings of months differed from the conventional definition of seasons. The wind and temperature analyses grouped the same months, with long summers, moderately long winters, short springs, and very s hort autumns. The precipitation analysis formed a long season, includi ng the winter months, representing synoptic systems occasionally passi ng through the area, a summer thunderstorm season associated with infl ux of moisture from the south, and dry transitional periods separating these seasons. The purpose of the analysis was to pregroup two years of hourly wind data to remove most of the annual signal before applyin g spatial eigenvector analysis for a mesoscale climatological classifi cation study. The approach is expected to be most useful when applied to mesoscale areas with significant seasonal variation in spatial patt erns of climatic variables.