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

Abstract

Despite recent advances in the classification of synoptic-scale events, there remains the need for development of a simple, automated, continental- scale air mass-based procedure. We present a new method of analysis to identify resident air masses at first-order weather stations to facilitate intersite comparison of daily air masses across a very large region. The ‘spatial synoptic classification’ (SSC) requires initial identification of the major air masses and their typical meteorological characteristics at each site. ‘Seed days’, which exhibit these typical characteristics for each air mass, are used as input for a linear discriminant function analysis, which produces a daily categorization of air masses with spatially continuous results. A second discriminant function analysis is used to determine whether a day is to be considered transitional between air masses. Using the SSC, air-mass frequencies 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 characteristics across the area. Keeping other factors constant, it appears that extended snow cover can reduce the temperature of a dry polar air mass by up to 11°C.