Multivariate Spatio-Temporal Clustering of Times-Series Data: An Approach for Diagnosing Cloud Properties and Understanding ARM Site Representativeness

A multivariate statistical clustering technique—based on the iterative k-means algorithm of Hartigan (Hartigan 1975)—has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs (Hargrove et al. 2001), the high performance parallel clustering algorithm (Hoffman and Hargrove 1999) was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km (Hargrove and Hoffman 1999). Figure 1 describes this application of the k-means approach to Multivariate Geographic Clustering (MGC).