Spatial grouping of United States climate stations using a hybrid clustering approach

Abstract

The development of a hybrid clustering technique based on the geographic proximity of observing stations and some application-driven measure of statistical similarity (in this case rank correlation) is described. The procedure is then applied to temperature and precipitation data from the United States (US) Historical Climatology Network. The resulting stations groups provide some insight into the number of observation stations that are necessary to monitor adequately the climate of the US. Based on temperature data alone, a 287-station subset of the original 1145 sites would be adequate to account for 80% of the spatial variability in seasonal temperature across the US. Geographically the distribution of these stations would be relatively sparse in the centre of the country with higher station density along the East Coast and from the Rocky Mountains to the West Coast. Generally, the temperature clusters match the existing US climate divisions to some extent. To monitor adequately the spatial variability of precipitation, a network of similar size could be used. However, such a network would only account for 65% of the spatial variability in precipitation. In this case, fairly uniform station density is indicated across the country with the highest station density in Florida and the Dakotas. A similar number of stations, but with slightly different geographic groupings would be adequate to monitor precipitation and temperature simultaneously. Copyright © 2001 Royal Meteorological Society.