A NEW SPATIAL SYNOPTIC CLASSIFICATION - APPLICATION TO AIR-MASS ANALYSIS

Despite recent advances in the classification of synoptic-scale events , there remains the need for development of a simple, automated, conti nental-scale air mass-based procedure. We present a new method of anal ysis to identify resident air masses at first-order weather stations t o facilitate intersite comparison of daily air masses across a very la rge region. The 'spatial synoptic classification' (SSC) requires initi al identification of the major air masses and their typical meteorolog ical characteristics at each site. 'Seed days', which exhibit these ty pical characteristics for each air mass, are used as input for a linea r discriminant function analysis, which produces a daily categorizatio n of air masses with spatially continuous results. A second discrimina nt function analysis is used to determine whether a day is to be consi dered transitional between air masses. Using the SSC, air-mass frequen cies were developed for winter across the USA east of the Rockies, and rates of modification were determined as each air mass traversed the region. The impact of snow cover on modification rates was determined by comparing two winter months with very different snow cover characte ristics across the area. Keeping other factors constant, it appears th at extended snow cover can reduce the temperature of a dry polar air m ass by up to 11 degrees C.