Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System during the 2005/06 Cool Season

Abstract

A simple binning technique developed to produce reliable probabilistic quantitative precipitation forecasts (PQPFs) from a multimodel short-range ensemble forecasting system is evaluated during the cool season of 2005/06. The technique uses forecasts and observations of 3-h accumulated precipitation amounts from the past 12 days to adjust the present day?s 3-h quantitative precipitation forecasts from each ensemble member for each 3-h forecast period. Results indicate that the PQPFs obtained from this simple binning technique are significantly more reliable than the raw (original) ensemble forecast probabilities. Brier skill scores and areas under the relative operating characteristic curve also reveal that this technique yields skillful probabilistic forecasts of rainfall amounts during the cool season. This holds true for accumulation periods of up to 48 h. The results obtained from this wintertime experiment parallel those obtained during the summer of 2004. In an attempt to reduce the effects of a small sample size on two-dimensional probability maps, the simple binning technique is modified by implementing 5- and 9-point smoothing schemes on the adjusted precipitation forecasts. Results indicate that the smoothed ensemble probabilities remain an improvement over the raw (original) ensemble forecast probabilities, although the smoothed probabilities are not as reliable as the unsmoothed adjusted probabilities. The skill of the PQPFs also is increased as the ensemble is expanded from 16 to 22 members during the period of study. These results reveal that simple postprocessing techniques have the potential to provide greatly improved probabilistic guidance of rainfall events for all seasons of the year.