暴雨集合预报-观测概率匹配订正法在四川盆地的应用研究

摘要

利用2008—2011年6—8月中国气象局T213全球集合预报24—240 h降水预报资料和四川盆地观测降水资料，提出四川盆地暴雨集合预报-观测概率匹配订正法。该方法将集合预报降水累积概率分布与观测降水累积概率分布进行概率匹配，对降水量为50 mm的集合预报平均值进行订正，获得暴雨预报订正值(A Calibrated Heavy Rainfall forecast value)，累积降水概率分布拟合函数采用Gamma函数。选取2013年6月28日—7月10日进行独立样本暴雨预报试验，分析四川盆地暴雨预报订正值分布特征和订正前后降水检验评分变化，讨论该方法存在的若干局限性。结果显示：T213集合预报对四川盆地降水预报存在预报量较观测量级小、模式预报时效越长降水预报越弱等系统性偏差，暴雨集合预报-观测概率匹配订正值普遍小于50 mm，且随预报时效延长而逐渐减小，有效地订正了T213暴雨集合预报系统性误差；暴雨集合预报-观测概率匹配订正法对“有或无暴雨”二分类暴雨预报改善较明显，ETs评分获得提高，且漏报率和空报率有所降低。%Using 24-240 h precipitation ensemble forecast produced by the T213 global ensemble prediction system (EPS) of China Meteo-rological Administration (CMA) and the precipitation observations between June and August from 2008 to 2011 in the Sichuan basin, we in-vestigate a heavy rainfall calibration method based on ensemble forecast-observation probability matching. The principle of this method is to correct model bias in heavy rainfall values (50 mm) based on a comparison of the probability density distributions of observed vs. ensemble forecasted precipitation amounts. A Corrected Heavy Rainfall forecast value(CALHR)over a model grid point is made by defining an adjust-ment to the forecast value in such a way that the adjusted cumulative forecast distribution matches the corresponding distribution observed. In particular, the Gamma function is used to simulate the probability density distributions of precipitation and to capture the bias information. This technique is used to perform the calibration of ensemble precipitation forecasts for the Sichuan basin from 28 June to 10 July 10 in 2013. The verified results are analyzed and its limitations are discussed. From this study, it was found that a noticeable systemic bias of precipitation forecasts for the T213 EPS system exists with smaller precipitation amounts than observations. The longer the forecast time is, the smaller the precipitation amounts will be. CALHR value generated by this method is generally smaller than 50 mm, and the longer the forecast time is, the smaller the CALHR values will be. This method is effectively to correct the systemic bias of heavy rain forecast for the T213 EPS system. Theheavy rainfall forecasts are improved for yes or no forecast for this method with higher ETs scores and lower missing rate and false alarm rate.