A SMIRNOV TEST-BASED CLUSTERING-ALGORITHM WITH APPLICATION TO EXTREMEPRECIPITATION DATA

A clustering algorithm is developed to form regions with similar extre me rainfall cumulative distribution function (CDF) characteristics. St ations are grouped on the basis of minimum geographic distance and acc eptance of the null hypothesis of equal CDF between all station pairs within a cluster. During each iteration previously clustered stations can be regrouped on the basis of the results of a suite of Smirnov tes ts. This process continues until all possible cluster mergers have bee n disallowed and thus the final number of clusters is determined solel y by the grouping process. The Smirnov test-based algorithm is applied to extreme rainfall data from West Virginia. The results are compared based on the L moments heterogeneity measure. With minor exceptions, the resulting subregions were deemed homogeneous by this measure. Thus it is possible that the Smirnov test-based clustering procedure can b e used as a guide for the otherwise subjective formation of precipitat ion regions that is a prerequisite of L moments distribution fitting r outines.