Three different precipitation ensemble configurations are first established from a great number of numerical experiments. These configurations are multianalysis (MA), multicumulus-scheme (MC), and multimodel (MM) configurations. A set of MA ensemble comes from the use of several different satellite-derived rain rates through the physical initialization procedure within the Florida State University Global Spectral Model (FSUGSM) system. Six different state-of-the art cumulus parameterization schemes are incorporated into the FSUGSM in order to briefly evaluate their performances on precipitation forecasts and introduce the MC ensemble configuration. The MM configuration of precipitation forecasts is composed of an FSU control forecast and those provided by five operational numerical weather prediction centers.

Results show that short- to medium-range superensemble (SE) forecasts of precipitation are invariably superior in skill to various conventional forecasts. A notably improved quantitative precipitation forecast is exhibited by a newly proposed SE technique in the present study. The MM configuration proved to be the most effective ensemble prediction system. Although a higher resolution SE forecast requires a large amount of computing time, it turns out that the impact is significant not only in skill scores, but also in resolving mesoscale-based convective disturbances. The advantage of the SE approach is also found to be evident in making probabilistic precipitation forecasts.