Abstract:

Based on the multi-mode precipitation gridded forecast data, observation data at meteorological stations of Qinghai Province and precipitation gridded analysis product of CMA multi-source precipitation analysis system (CMPAS), the prediction performance of models to heavy precipitation cases in Qinghai Province from July to August 2020 were comparatively tested by using traditional verification method such as threat score (TS) and spatial verification method such as fraction skill score (FSS) of neighborhood method and object-oriented diagnostic evaluation method (MODE). The main conclusions are as follows: (1) The traditional TS scores of global model of European Center of Medium-range Weather Forecasts (ECMWF) and National Center for Environmental Prediction (NCEP), China Meteorological Administration global assimilation forecast system (CMA-GFS) and GRAPES regional mesoscale numerical prediction system (GRAPES-Meso) to light rain and above were higher, and the prediction performance difference of four models to light rain was little, but the models with the highest score under different verification methods were slightly different. (2) Compared with the observation, the forecasted locations of four models to moderate rain and above were generally to the west. The traditional TS scores of moderate rain and above were significantly different, but the performance score of models under different verification methods was relatively consistent. (3) Compared with the observation, the forecasted location of four models to heavy rain and above was generally to the north. The prediction ability of each model to heavy rain and above was poor, and the traditional TS scores of heavy rain and above were equal to 0, while FSS scores could effectively improve the evaluation ability to models difference, and MODE could give the specific performance of corresponding object attributes, which provides valuable reference for model application, but it was more sensitive to the selection of verification parameters.

Key words: traditional verification, FSS, MODE, heavy precipitation, Qinghai Province

摘要：

基于多模式降水格点预报资料、青海省气象站实况资料及多源融合降水格点分析产品,针对青海省2020年7—8月强降水天气个例,采用TS(threat score)评分等传统检验方法和FSS(fraction skill score)评分及MODE(method of object-based diagnostic evaluation)空间检验方法,对比检验各模式在青海强降水中的预报性能。结果表明:(1)小雨及以上量级,欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)全球模式、中国气象局全球同化预报系统(CMA-GFS)及GRAPES区域中尺度数值预报系统(GRAPES-Meso)的传统TS评分均较高且预报能力相差不大,但不同检验方法下评分最高的模式略有不同;(2)中雨及以上量级,各模式预报较观测普遍偏西,且传统TS评分差异较为明显,但不同检验方法下模式评分优劣表现较为一致;(3)大雨及以上量级,各模式预报较观测普遍偏北,且预报能力较差,传统TS评分为0,FSS评分有效提高了模式差异性的评估能力,MODE方法则给出了预报和观测对象属性的具体表现,但对检验参数的选取较为敏感。

关键词: 传统检验, FSS, MODE, 强降水, 青海