SHORT-RANGE ENSEMBLE FORECASTING OF QUANTITATIVE PRECIPITATION

The impact of initial condition uncertainty (ICU) on quantitative prec ipitation forecasts (QPFs) is examined for a case of explosive cycloge nesis that occurred over the contiguous United States and produced wid espread, substantial rainfall. The Pennsylvania State University-Natio nal Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 ( MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boun dary conditions for MM4 are provided by ensemble forecasts from a glob al spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analy sis error, but they are not dynamically conditioned. Results for the 8 0-km ensemble forecast are compared to forecasts from the then operati onal Nested Grid Model (NGM), a single 40-km/15-layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensembl e based on a different cumulus parameterization and slightly different unperturbed initial conditions. Large sensitivity to ICU marks ensemb le QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.00 ''. Ensemble averaging reduces the root-mean-square e rror (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8-10. Ensemble averaging can adversely aff ect the bias and equitable threat scores, however, because of its smoo thing nature. Probabilistic forecasts for five mutually exclusive, com pletely exhaustive categories are found to be skillful relative to a c limatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-ran ge ensemble forecasting (SREF) techniques exceed any due to doubling t he resolution, and the error growth due to ICU greatly exceeds that du e to different resolutions. If the authors' results are representative , they indicate that SREF can now provide useful QPF guidance and incr ease the accuracy of QPF when used with current analysis-forecast syst ems.