Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

Abstract

A new and efficient method for identifying ?rogue? air temperature stations?locations with unusually large air temperature trends?is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.