Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System

Abstract

A simple binning technique is developed to produce reliable 3-h probabilistic quantitative precipitation forecasts (PQPFs) from the National Centers for Environmental Prediction (NCEP) multimodel short-range ensemble forecasting system obtained during the summer of 2004. The past 12 days? worth of forecast 3-h accumulated precipitation amounts and observed 3-h accumulated precipitation amounts from the NCEP stage-II multisensor analyses are used to adjust today?s 3-h precipitation forecasts. These adjustments are done individually to each of ensemble members for the 95 days studied. Performance of the adjusted ensemble precipitation forecasts is compared with the raw (original) ensemble predictions. Results show that the simple binning technique provides significantly more skillful and reliable PQPFs of rainfall events than the raw forecast probabilities. This is true for the base 3-h accumulation period as well as for accumulation periods up to 48 h. Brier skill scores and the area under the relative operating characteristics curve also indicate that this technique yields skillful probabilistic forecasts. The performance of the adjusted forecasts also progressively improves with the increased accumulation period. In addition, the adjusted ensemble mean QPFs are very similar to the raw ensemble mean QPFs, suggesting that the method does not significantly alter the ensemble mean forecast. Therefore, this simple postprocessing scheme is very promising as a method to provide reliable PQPFs for rainfall events without degrading the ensemble mean forecast.