Improving tropical precipitation forecasts from a multianalysis superensemble

This paper utilizes forecasts from a multianalysis system to construct a su perensemble of precipitation forecasts. This method partitions the computat ions into two time lines. The first of those is a control (or a training) p eriod and the second is a forecast period. The multianalysis is derived fro m a physical initialization-based data assimilation of "observed rainfall r ates." The different members of the reanalysis are produced by using differ ent rain-rate algorithms for physical initialization. The basic rain-rate d atasets are derived from satellites' microwave radiometers, including those from the Tropical Rainfall Measuring Mission (TRMM) satellites and the Spe cial Sensor Microwave Imager (SSM/I) data from three current U.S. Air Force Defense Meteorological Satellite Program (DMSP) satellites. During the tra ining period, 155 experiments were conducted to find the relationship betwe en forecasts from the multianalysis dataset and the best "observed" estimat es of daily rainfall totals. This relationship is based on multiple regress ion and defined by statistical weights (which vary in space.) The forecast phase utilizes the multianalysis forecasts and the statistics from the trai ning period to produce superensemble forecasts of daily rainfall totals. Th e results for day 1, day 2, and day 3 forecasts are compared to various con ventional forecasts with a global model. The superensemble day 3 forecasts of precipitation clearly have the highest skill in such comparisons.