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A new measure of ensemble performance: Perturbation versus error correlation analysis (PECA)

Abstract Most existing ensemble forecast verification statistics are influenced by the quality of not only the ensemble generation scheme, but also the forecast model and the analysis scheme. In this study, a new tool called perturbation versus error correlation analysis (PECA) is introduced that lessens the influence of the initial errors that affect the quality of the analysis. PECA evaluates the ensemble perturbations, instead of the forecasts themselves, by measuring their ability to explain forecast error variance. As such, PECA offers a more appropriate tool for the comparison of ensembles generated by using different analysis schemes. Ensemble perturbations from both the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated and found to perform similarly. The error variance explained by either ensemble increases with the number of members and the lead time. The dynamically conditioned NCEP and ECMWF perturbations outpe...