The applicability of synoptic classifications: Analysis of an urban complex

Synoptic air mass classifications are applicable to a variety of climatological issues, yet the spatial applicability of synoptic classifications has received little attention, particularly within complex urban areas. This study analyzed spatio-temporal temperature vagaries across a spatially extensive urban built environment, as delineated by a synoptic air mass classification scheme, the Spatial Synoptic Classification (SSC).; Climatological data were assembled from twenty meteorological stations within the Phoenix, Arizona metropolitan region, as well as from the region's first-order station, Sky Harbor Airport. The SSC classifications utilized in this research, the Dry Moderate (DM), the Dry Tropical (DT), and the Moist Tropical (MT), were generated from meteorological observations and forecasts from Sky Harbor Airport. Based on the period of record at the stations, the period of study for this research was 1992 to 2002. The data from the twenty assembled meteorological stations were analyzed to identify the nature of the climatological relationship between each station and Sky Harbor Airport during the selected SSC classifications.; While temperature variation was anticipated at the meteorological stations situated across the built environment, the statistical relationship between the stations and Sky Harbor was fairly systematic, regardless of SSC classification or season. The spatial and temporal temperature variation that existed at the meteorological stations during the study period occurred largely in response to the physical structure of the built environment and distance from Sky Harbor Airport. Overall, temperature variability was suppressed during MT classifications and enhanced during DT classifications. Changes in elevation within the study region only played a minimal role in affecting temperature at the stations, and only during synoptically dry conditions. With consideration to a proxy land cover variable that also implied distance and direction from Sky Harbor, on average more than seventy percent of the stations' temperature could be predicted by Sky Harbor meteorological conditions as delineated by SSC classification.; This research tested the spatio-temporal temperature variation across a physically-heterogeneous mesoscale environment as delineated by three SSC classes. Given that the temperature variation was systematic and largely predictable, the SSC could be applicable to a variety of social forecasts in complex urban centers, particularly during the more oppressive MT classification.