An Investigation of the Potential of Component Analysis for Weather Classification*

Abstract Selected hourly surface observations from Madison, Wis. and Minneapolis-St. Paul, Minn. are used as basic data for a series of analyses to determine the feasibility of establishing weather classifications. Component analysis (factor analysis) is applied to a sample of January data for Madison to reduce the number of variables needed to suitably describe each day meteorologically and to create orthogonality among these new variables. With these results as the design matrix in regression analysis, a mathematical model for each day is constructed and each day is compared to all other days in order to classify similar days into distinctive weather types. Every day within each class is compared with the synoptic situation for that day to establish whether these types form a reasonable synoptic pattern. The temporal and spatial validity of these newly found weather types is tested by applying the foregoing results to an independent January sample for Madison and an independent January sample for Minnea...