A Strategy for Verification of Weather Element Forecasts from an Ensemble Prediction System

Abstract

Using a Bayesian context, new measures of accuracy and skill are proposed to verify weather element forecasts from ensemble prediction systems (EPSs) with respect to individual observations. The new scores are in the form of probabilities of occurrence of the observation given the EPS distribution and can be applied to individual point forecasts or summarized over a sample of forecasts. It is suggested that theoretical distributions be fit to the ensemble, assuming a shape similar to the shape of the climatological distribution of the forecast weather element. The suggested accuracy score is simply the probability of occurrence of the observation given the fitted distribution, and the skill score follows the standard format for comparison of the accuracy of the ensemble forecast with the accuracy of an unskilled forecast such as climatology. These two scores are sensitive to the location and spread of the ensemble distribution with respect to the verifying observation. The new scores are illustrated using the output of the European Centre for Medium-Range Weather Forecasts EPS. Tests were carried out on 108 ensemble forecasts of 2-m temperature, precipitation amount, and windspeed, interpolated to 23 Canadian stations. Results indicate that the scores are especially sensitive to location of the ensemble distribution with respect to the observation; even relatively modest errors cause a score value significantly below the maximum possible score of 1.0. Nevertheless, forecasts were found that achieved the perfect score. The results of a single application of the scoring system to verification of ensembles of 500-mb heights suggests considerable potential of the score for assessment of the synoptic behavior of upper-air ensemble forecasts. The paper concludes with a discussion of the new scoring method in the more general context of verification of probability distributions.