Calibrated probabilistic quantitative precipitation forecasts based on theMRF ensemble

Probabilistic quantitative precipitation forecasts (PQPFs) based on the Nat ional Centers for Environmental Prediction Medium-Range Forecast (MRF) ense mble currently perform below their full potential quality (i.e., accuracy a nd reliability). This unfulfilled potential is due to the MRF ensemble bein g adversely affected by systematic errors that arise from an imperfect fore cast model and less than optimum ensemble initial perturbations. This resea rch sought to construct a calibration to account for these systematic error s and thus produce higher quality PQPFs. The main tool of the calibration was the verification rank histogram, which can be used to interpret and adjust an ensemble forecast. Using a large tr aining dataset, many histograms were created, each characterized by a diffe rent forecast lead time and level of ensemble variability. These results we re processed into probability surfaces, providing detailed information on p erformance of the ensemble as part of the calibration scheme. Improvement of the calibrated PQPF over the current uncalibrated PQPF was e xamined using a separate, large forecasting dataset, with climatological PQ PF as the baseline. While the calibration technique noticeably improved the quality of PQPF and extended predictability by about 1 day, its usefulness was bounded by the intrinsic predictability limits of cumulative precipita tion. Predictability was found to be dependent upon the precipitation categ ory. For significant levels of precipitation (high thresholds), the calibra tion designed in this research was found to be useful only for short-range PQPFs. For low precipitation thresholds, the calibrated PQPF did prove to b e of value in the medium range.