陈静,刘凑华,陈法敬,韦青,李嘉鹏,赵滨,杨东,张志刚. 2019. 一种基于可预报性的暴雨预报评分新方法Ⅰ:中国暴雨可预报性综合指数[J]. 气象学报, 77(1):15-27, doi:10.11676/qxxb2019.002
一种基于可预报性的暴雨预报评分新方法Ⅰ:中国暴雨可预报性综合指数
A new verification method for heavy rainfall forecast based on predictability Ⅰ: Synthetic predictability index of heavy rainfall in China
投稿时间:2017-12-13  修订日期:2018-06-21
DOI:10.11676/qxxb2019.002
中文关键词:  中国暴雨  暴雨气候频率  暴雨面积比率  数值模式暴雨评分  可预报性综合指数
英文关键词:Heavy rainfall over China  Climate frequency  Area ratio of rainstorm  Scores of heavy rainfall of numerical model  Synthetic predictability index of heavy rainfall
基金项目:中国气象局气象预报业务关键技术发展专项(YBGJXM201706)、国家科技支撑计划项目(2015BAC03B01)、国家重点基础研究发展计划973项目(2012CB417204)。
作者单位E-mail
陈静 国家气象中心, 北京, 100081  
刘凑华 国家气象中心, 北京, 100081 liucouhua@163.com 
陈法敬 国家气象中心, 北京, 100081  
韦青 国家气象中心, 北京, 100081  
李嘉鹏 浙江省气象台, 杭州, 310002  
赵滨 国家气象中心, 北京, 100081  
杨东 山西省气象局, 太原, 030002  
张志刚 中国气象局, 北京, 100081  
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中文摘要:
      针对当前暴雨预报检验采用二分类事件检验方法存在的双重惩罚导致评分过低,没有考虑到中国暴雨可预报性时、空分布不均,不便于对比分析不同区域暴雨预报能力差异等问题,为了发展基于可预报性的新型暴雨预报评分方法,在综合分析影响预报员暴雨预报信心的主要因素(暴雨气候统计特征、天气影响系统运动尺度特征及数值模式预报能力等)基础上,利用2008—2016年4—10月中国国家气象信息中心5 km×5 km分辨率的多源降水融合格点分析资料、站点降水观测资料和中国国家级业务区域模式降水预报资料以及扩展空间暴雨样本统计方法,构建了一种新型的中国暴雨可预报性综合指数(Synthetic Predictability Index of Heavy Rainfall,以下简称SPI)数学模型,以定量描述中国各区域的暴雨可预报性特征。SPI数学模型由暴雨气候频率、暴雨面积比率和模式暴雨预报成功指数(Threat Score,TS)3个分量组成,计算了2008—2016年4—10月SPI的3个分量及其时、空变化特征。分析结果显示:暴雨面积比率对SPI的时间和空间变化影响最大,两者偏相关系数大于0.9;其次是暴雨气候频率的影响,两者偏相关系数值为0.8左右;第三是模式暴雨预报TS评分的影响,两者的偏相关系数为0.7左右。分析还发现,SPI大值区随季节而变化,空间分布不均匀:4—5月,可预报性大值区主要分布在华南地区;6—7月,主要分布在江淮流域; 7月中旬至8月,大值中心从江淮北部移到华北和东北地区;9月,副热带高压南撤,大值中心也相应南撤。
英文摘要:
      To meet the requirement of developing a new method for evaluating the forecast skill of heavy rainfall, the main factors affecting forecaster's confidence in heavy rainstorm forecasting, that is, the forecasting ability of statistical characteristics of the heavy rainstorm climate, the characteristics of movement scale of systems influencing heavy rainstorms and the numerical prediction model, are considered in the present study to develop a new mathematical model of Synthetic Predictability Index of Heavy Rain (SPI). The SPI is composed of three components:rainstorm climatic frequency, rainstorm area ratio and numerical model rainstorm forecasting success index (Threat Score, TS). It is established based on analysis of 5 km×5 km resolution multi-source precipitation fusion grid analysis data, precipitation observation data at weather stations, precipitation forecast data produced by operational model on regional scale and the statistical method of extended space rainstorm samples of the National Meteorological Information Center. The three components of SPI and their spatial-temporal variations during April-October from 2008 to 2016 are calculated. The results show that heavy rainfall changes with season and its spatial distribution is not uniform. From April to May, the more predictable areas are mainly distributed in southern China; from June to July, the more predictable areas are mostly located in the Changjiang-Huaihe river basins; from mid-July to August, they are largely found in North and Northeast China. In September, following the southward retreat of the subtropical high pressure, the large value center of SPI moves southward correspondingly. In addition, the partial correlation coefficients between the rainstorm predicta-bility index and the three components shows that the partial correlation coefficient between the SPI and the storm area ratio is the highest with the value higher than 0.9. The comprehensive index of rainstorm predictability in China has laid a footstone for the development of verification scores of rainstorm forecasting based on predictability.
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