DELINEATION OF MESOSCALE CLIMATE ZONES IN THE NORTHEASTERN UNITED-STATES USING A NOVEL-APPROACH TO CLUSTER-ANALYSIS

Climate regions within the northeastern United States are defined usin g a combination of multivariate Statistical techniques. A set of over 100 climatic variables from 641 United States and Canadian Cooperative Observer Network stations form the basis for the classification. Usin g various numbers of retained principal components; a suite of hierarc hical clustering solutions is produced using Ward's method. A single 5 4-cluster solution is selected based upon the similarity of cluster ou tcomes using sequentially larger principal component datasets. These c lusters form a set of seeds that are used to derive a final nonhierarc hical cluster solution. A novel approach is used in the nonhierarchica l cluster analysis to reduce bias introduced by both redundant and irr elevant data. A sequence of cluster solutions is developed in which an additional principal component is considered in each successive solut ion. Final cluster membership is assigned based on the maximum frequen cy of cluster membership within this array of solutions. Approximately one-fourth of the climatological stations change cluster membership a s a result of this nonhierarchical clustering procedure. These changes result in substantial improvements to the spatial homogeneity of the clusters. Marginal improvements to within- and between-cluster standar d deviation are also realized. Once a final grouping of stations is es tablished, discriminant functions are calculated to distinguish the cl imatic zones in terms of variables derived from latitude, longitude, a nd elevation. Cross validation shows that more than 60% of the station s are correctly classified based on the discriminant functions. Since the spatial resolution of the 641 climatological stations is relativel y low, a 5-min grided elevation dataset was used in conjunction with t he discriminant functions to produce the final climate delineations.