| description abstract | Most climatological datasets are beset with urban temperature influences that distort long-term trends. Using an hourly dataset of 41 urban and rural stations from the United States, discriminant functions were developed using diurnal temperature range indices based on temperature, dewpoint, and dewpoint depression that capture the differences between the two environments. Based on data for 1997?2006, diurnal temperature range and nighttime dewpoint depression range indices provide the best classification variables to statistically discriminate between urban and rural climates. Of the 41 stations, 93% were correctly classified by this technique in a cross-validation analysis. An additional discriminant analysis specific to coastal stations was needed because coastal climates were noted to be aberrant. Here, all stations tested were correctly classified by the procedure. Temporal trends in discriminant scores indicate periods of time during which urbanization was occurring or increasing. Instrumental and location changes were noted to affect both temperature and dewpoint series and therefore the classification. However, such discontinuities can potentially be adjusted and the homogenized data used with the classification technique. The use of this data-driven approach complements existing methods used to classify the urban character of stations, because it is objective, is applicable in the presatellite era, and can infer changes at higher temporal resolution. | |
