摘要:2019年，数值预报中心开发了以GRAPES全球模式为驱动场，集合变换卡尔曼滤波为初值扰动方法，随机物理过程倾向项为模式扰动方法的10 km水平分辨率GRAPES-REPS V3.0区域集合预报模式，并投入业务运行。基于该模式，作者开展了2019年7～9月夏季降水不确定性的集合预报实时试验，并从统计检验和个例分析角度，与GRAPES-REPS V2.0和ECMWF全球集合预报模式进行对比，由此对GRAPES-REPS V3.0区域集合预报模式的降水预报能力给予客观评价，并分析了引起中尺度强降水预报不确定性的物理机制，研究结论可为诊断集合预报模式及改进集合预报方法提供依据。结果表明：（1）GRAPES-REPS V3.0区域集合预报系统的降水ETS评分在所有预报时效和量级内均优于GRAPES-REPS V2.0区域集合预报模式，降水成员具有明显等同性，且概率预报技巧FSS评分较高，GRAPES-REPS V3.0区域集合预报模式降水预报效果全面优于GRAPES-REPS V2.0区域集合预报模式。（2）GRAPES-REPS V3.0区域集合预报的集合平均降水BIAS评分及小雨和暴雨ETS评分均明显优于ECMWF全球集合预报系统，降水概率预报与ECMWF降水概率具有一定可比性。（3）个例分析结果表明，不同集合预报模式通过刻画中尺度特征物理量不确定性来捕捉降水预报不确定性，初始时刻，GRAPES-REPS V3.0区域集合预报模式和ECMWF全球集合预报模式环流形势分布较为相似，随预报时效演变，GRAPES-REPS V3.0区域集合预报模式对中尺度动力、热力场捕捉更为准确，相应地对降水落区与量级预报较好，概率预报技巧较优。（4）与ECMWF全球集合预报模式相比，GRAPES区域集合预报模式集合成员能很好地预报降水发生、发展、消亡整个过程，故GRAPES-REPS V3.0区域集合预报系统对中国汛期降水具有较强的预报能力。
关键词:GRAPES-REPS区域集合预报模式/
降水预报/
检验
Abstract:In this study, we developed a regional ensemble prediction system [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS V3.0)] with a horizontal resolution of 10 km, which was put into operation by the Numerical Weather Prediction Center of the China Meteorological Administration in 2019. The background field of the GRAPES-REPS V3.0 is the GRAPES global model. The initial perturbation and model perturbation methods used are the ensemble transform Kalman filter and the stochastic perturbed parameterization tendencies, respectively. The system was run in real time for the 2019 summer season (July through September) and its results were compared with those obtained by the GRAPES-REPS V2.0 and ECMWF global ensemble prediction systems by statistical verification and case analysis to objectively and comprehensively evaluate its precipitation forecast skill and forecast uncertainties. We also analyzed the physical mechanism responsible for the forecast uncertainties regarding meso-scale intense precipitation. The study results, which provide a basis for diagnosing regional ensemble prediction systems and developing ensemble forecast methods, can be summarized as follows: (1) GRAPES-REPS V3.0 model obtained better precipitation Equitable Threat Scores (ETS) than the GRAPES-REPS V2.0 model in terms of the forecast lead times and rainfall classes with more equal rainfall members. The probability forecast fraction skill scores were also better. As such, the precipitation forecast skills of the GRAPES-REPS V3.0 model are better overall than those of the GRAPES-REPS V2.0 model. (2) The ensemble mean precipitation bias and ETS scores of the GRAPES-REPS V3.0 for light rain and rainstorm are better than those obtained by the ECMWF global ensemble forecast system, and the probability forecast skill of the two models is comparable. (3) The case studies show that different ensemble prediction systems capture precipitation forecast uncertainties by describing the uncertainties of meso-scale physical quantities. At an initial lead time, the circulation patterns of the GRAPES-REPS V3.0 regional ensemble prediction system and the ECMWF global ensemble prediction system are similar. However, with the evolution of the forecast lead time, the GRAPES-REPS V3.0 ensemble prediction model better describes the meso-scale dynamic and thermal fields, with more accurate rainfall locations and magnitudes and a better probabilistic forecast result. (4) Compared with the ECMWF model, the ensemble members of the GRAPES-REPS V3.0 model can effectively forecast the occurrence, development, and extinction of rainfall processes. As such, the GRAPES-REPS V3.0 model shows higher skill in forecasting precipitation during the Chinese flood season.
Key words:GRAPES-REPS model/
Precipitation forecast/
Verification
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