| description abstract | Spatially homogeneous climate regions were developed from long-term monthly temperature and precipitation data for a subset of the U.S. Northern Plains. Climate regions were initially defined using the ?best? of three agglomerative and hierarchical clustering methodologies, then the clusters were objectively modified using a ?pseudohierarchical? iterative improvement technique. Under the premise of hierarchical cluster analysis, once an object has been assigned to a cluster, it cannot later he reassigned to a different cluster, even if it is statistically desirable. The objective modification technique used herein is employed to compensate for this problem. Principal component analysis (PCA) was used to reduce a 147-station dataset, consisting of 24 climatic variables averaged over the 1931?1990 period, to three orthogonal components. The new standardized mars, which explain 93% of the original dataset variance, were then subjected to the Ward's, average linkage, and complete linkage clustering methods. The average linkage method produced the most representative statistical results in identifying the climate regions. An iterative improvement technique was then utilized to test ?border station? membership and to modify the climate region houses. Fifteen climate regions resulted from the clustering (with two single-station clusters in the Black Hills alone), although they age just one possible partitioning of the data. The within-cluster variability is generally the same for the 15 climate regions and the corresponding 21 National Climatic Data Center (NCM) climate divisions. However, since data within-cluster variability tends to decrease with increasing cluster number, this result favors the new climate regions. Additionally, the new climate regions am shown to be superior to the NCDC climate, divisions in wont of between-cluster variability. These results suggest that the NCDC climate divisions could be redefined, improving their climatic homogeneity. | |
