Verification of eta-RSM short-range ensemble forecasts

Motivated by the success of ensemble forecasting at the medium range, the performance of a prototype short-range ensemble forecast system is examined. The ensemble dataset consists of 15 case days from September 1995 through January 1996. There are 15 members of the ensemble, 10 from an 80-km version of the era model and five from the regional spectral model. Initial conditions include various in-house analyses available at the National Centers for Environmental Prediction as well as bred initial conditions interpolated from the medium-range forecast ensemble. Forecasts from the 29-km mesoeta model were archived as well for comparison. The performance of the ensemble is first evaluated by the criterion of ''uniformity of verification rank.'' Assuming a perfect forecast model, equally plausible initial conditions, and the verification is a plausible member of the ensemble, these imply the verification when pooled with the 15 ensemble forecasts and sorted is equally likely to occur in each of the 16 ranks. Hence, over many independent samples, a histogram of the rank distribution should be nearly uniform. Using data from the ensemble forecasts, rank distributions were populated and found to be nonuniform. This was determined to be largely a result of model and initial condition deficiencies and not problems with the verification data. The uniformity of rank distributions varied with atmospheric baroclinicity for midtropospheric forecast variables but not for precipitation forecasts. Examination of the error characteristics of individual ensemble members showed that the assumption of identical errors for each member is not met with this particular ensemble configuration, primarily because of the use of both bred and nonbred initial conditions in this test, further, there were both differences in the accuracy of eta and regional spectral model bred member forecasts. The performance of various summary forecasts from the ensemble such as its mean showed that the ensemble can generate forecasts that have similar or lower error than forecasts from the 29-km mesoeta, which was approximately equivalent in computational expense. Also, by combining the ensemble forecasts with rank information from other cases, reliable ensemble precipitation forecasts could be created, indicating. the potential for useful probabilistic forecasts of quantitative precipitation from the ensemble.