Calibrated Probabilistic Quantitative Precipitation Forecasts Based on theMRF Ensemble

Abstract

Probabilistic quantitative precipitation forecasts (PQPFs) based on the National Centers for Environmental Prediction Medium-Range Forecast (MRF) ensemble currently perform below their full potential quality (i.e., accuracy and reliability). This unfulfilled potential is due to the MRF ensemble being adversely affected by systematic errors that arise from an imperfect forecast model and less than optimum ensemble initial perturbations. This research sought to construct a calibration to account for these systematic errors 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 training dataset, many histograms were created, each characterized by a different forecast lead time and level of ensemble variability. These results were processed into probability surfaces, providing detailed information on performance of the ensemble as part of the calibration scheme. Improvement of the calibrated PQPF over the current uncalibrated PQPF was examined using a separate, large forecasting dataset, with climatological PQPF 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 precipitation. Predictability was found to be dependent upon the precipitation category. For significant levels of precipitation (high thresholds), the calibration designed in this research was found to be useful only for short-range PQPFs. For low precipitation thresholds, the calibrated PQPF did prove to be of value in the medium range.