The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature

Previous methods for creating consensus forecasts weight individual ensemble members based upon their relative performance over the previous N days, implicitly making a short-term persistence assumption about the underlying flow regime. A postprocessing scheme in which model performance is linked to underlying weather regimes could improve the skill of deterministic ensemble model consensus forecasts. Here, principal component analysis of several synoptic-and mesoscale fields from the North American Regional Reanalysis dataset provides an objective means for characterizing atmospheric regimes. Clustering techniques, including K-means and a genetic algorithm, are developed that use the resulting principal components to distinguish among the weather regimes. This pilot study creates a weighted consensus from 48-h surface temperature predictions produced by the University of Washington Mesoscale Ensemble, a varied-model (differing physics and parameterization schemes) multianalysis ensemble with eight members. Different optimal weights are generated for each weather regime. A second regime-dependent consensus technique uses linear regression to predict the relative performance of the ensemble members based upon the principal components. Consensus forecasts obtained by the regime-dependent schemes are compared using cross validation with traditional N-day ensemble consensus forecasts for four locations in the Pacific Northwest, and show improvement over methods that rely on the short-term persistence assumption.