The Use of Ensembles to Identify Forecasts with Small and Large Uncertainty

Abstract

In the past decade ensemble forecasting has developed into an integral part of numerical weather prediction. Flow-dependent forecast probability distributions can be readily generated from an ensemble, allowing for the identification of forecast cases with high and low uncertainty. The ability of the NCEP ensemble to distinguish between high and low uncertainty forecast cases is studied here quantitatively. Ensemble mode forecasts, along with traditional higher-resolution control forecasts, are verified in terms of predicting the probability of the true state being in 1 of 10 climatologically equally likely 500-hPa height intervals. A stratification of the forecast cases by the degree of overall agreement among the ensemble members reveals great differences in forecast performance between the cases identified by the ensemble as the least and most uncertain. A new ensemble-based forecast product, the ?relative measure of predictability,? is introduced to identify forecasts with below and above average uncertainty. This measure is standardized according to geographical location, the phase of the annual cycle, lead time, and also the position of the forecast value in terms of the climatological frequency distribution. The potential benefits of using this and other ensemble-based measures of predictability is demonstrated through synoptic examples.