| With current computational limitations, the accuracy of high resolution precipitation forecasts has limited temporal and spatial resolutions. Forecast accuracy drops dramatically after a 24 hour forecast. Current operational mesoscale models run only to 48--72 hours. However, with the recent development of the superensemble technique, the potential to improve precipitation forecasts at the regional resolution exists. The purpose of this study is to apply the superensemble technique to regional precipitation forecasts to generate more accurate forecasts pinpointing exact locations and intensities of strong precipitation systems. This study will determine the skill and predictability of a regional superensemble forecast out to 60 hours.; Precipitation results were stratified by time of day to allow detections of the diurnal cycle. As expected, warm season daytime precipitation is commonly forced by convection which is difficult to accurately model. Results were also stratified by lead time which reveals how quickly the forecasts degrade in time. Currently, mesoscale models such as those utilized in the ensemble are approaching the limits of precipitation predictability. Major synoptic regimes, including subtropical high, mid-latitude trough/front, and tropical cyclone, were examined to determine the skill of the superensemble under various synoptic conditions. Finally, different rainfall intensities were examined which revealed the superensemble forecast significantly improved the forecast at significant rainfall amounts.; The regional superensemble consists of 12 to 60-hour daily quantitative precipitation forecasts from 6 models. Five are independent operational models, and one comes from the physical-initialized FSU regional spectral model. The superensemble forecasts are verified during the summer 2003 season over the southeastern US using a merged RFC Stage IV radar/gauge and satellite analyses. Precipitation forecasts were skillful in outperforming the operational models at all model times. Skill measurements that were examined include ETS, Bias, FAR, and POD. |
