Open AccessComparison of Probabilistic Quantitative Precipitation Forecasts from Two Postprocessing Mechanisms
Author(s) -
Yu Zhang,
Limin Wu,
Michael Scheuerer,
John C. Schaake,
Cezar Kongoli
Publication year - 2017
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm-d-16-0293.1
Subject(s) - probabilistic logic , precipitation , reliability (semiconductor) , probabilistic forecasting , computer science , range (aeronautics) , ensemble forecasting , statistics , quantitative precipitation forecast , distribution (mathematics) , probability distribution , econometrics , environmental science , meteorology , artificial intelligence , mathematics , power (physics) , mathematical analysis , physics , materials science , composite material , quantum mechanics
This article compares the skill of medium-range probabilistic quantitative precipitation forecasts (PQPFs) generated via two postprocessing mechanisms: 1) the mixed-type meta-Gaussian distribution (MMGD) model and 2) the censored shifted Gamma distribution (CSGD) model. MMGD derives the PQPF by conditioning on the mean of raw ensemble forecasts. CSGD, on the other hand, is a regression-based mechanism that estimates PQPF from a prescribed distribution by adjusting the climatological distribution according to the mean, spread, and probability of precipitation (POP) of raw ensemble forecasts. Each mechanism is applied to the reforecast of the Global Ensemble Forecast System (GEFS) to yield a postprocessed PQPF over lead times between 24 and 72 h. The outcome of an evaluation experiment over the mid-Atlantic region of the United States indicates that the CSGD approach broadly outperforms the MMGD in terms of both the ensemble mean and the reliability of distribution, although the performance gap tends to be narrow, and at times mixed, at higher precipitation thresholds (>5 mm). Analysis of a rare storm event demonstrates the superior reliability and sharpness of the CSGD PQPF and underscores the issue of overforecasting by the MMGD PQPF. This work suggests that the CSGD’s incorporation of ensemble spread and POP does help enhance its skill, particularly for light forecast amounts, but CSGD’s model structure and its use of optimization in parameter estimation likely play a more determining role in its outperformance.