Synoptic Map-Pattern Classification Using Recursive Partitioning and Principal Component Analysis

Abstract

Abstract A method for classifying synoptic-scale maps into discrete groups is introduced. Tree-based recursive partitioning models are used to develop mappings between synoptic-scale circulation fields and the leading linear and nonlinear principal components (PCs) of weather elements observed at a surface station. Statistically unique but climatically insignificant patterns are avoided by identifying map patterns based on their association with indices related to local weather conditions. The method requires few user-adjustable parameters and includes an algorithm that provides objective guidance for determining the appropriate number of map patterns to retain. The classification method is demonstrated using daily sea level pressure and 500-hPa geopotential height maps from a domain covering British Columbia and the northeastern Pacific Ocean. The linear and nonlinear weather element PCs are derived from daily measurements of surface temperature, dewpoint temperature, cloud opacity, and u and υ wind comp...