Semi‐supervised multivariate regression trees: putting the ‘circulation’ back into a ‘circulation‐to‐environment’ synoptic classifier

Abstract

AbstractMultivariate regression trees (MRTs) have been used in synoptic climatology to construct “circulation‐to‐environment” synoptic classifications. Because the goal of an MRT is to maximize discrimination of the environmental predictand variables, performance in terms of the synoptic‐scale circulation predictors is typically sacrificed. This paper introduces a semi‐supervised approach in which a weighted combination of synoptic‐scale predictors and environmental variables serve as predictands in a MRT. Results for southern British Columbia, Canada, indicate that (1) a semi‐supervised MRT can outperform a fully supervised MRT in terms of discrimination of the surface environment; (2) weighting allows the synoptic classifier to behave as a fully unsupervised model, a fully supervised model, or intermediate between the two ends of the spectrum; and (3) the optimum trade‐off between circulation and environment must be chosen by the user depending on specific needs. © 2011 Crown in the right of Canada. Published by John Wiley & Sons Ltd.