陈法敬,陈静,韦青,李嘉鹏,刘凑华,杨东,赵滨,张志刚. 2019. 一种基于可预报性的暴雨预报评分新方法Ⅱ:暴雨检验评分模型及评估试验[J]. 气象学报, 77(1):28-42, doi:10.11676/qxxb2019.003
一种基于可预报性的暴雨预报评分新方法Ⅱ:暴雨检验评分模型及评估试验
A new verification method for heavy rainfall forecast based on predictability Ⅱ: Verification method and test
投稿时间：2017-12-13  修订日期：2018-06-22
DOI：10.11676/qxxb2019.003
中文关键词:  暴雨评分基函数  暴雨评分模型  邻域匹配  距离权重  暴雨预报检验
英文关键词:Kernal function of heavy rainfall scoring  Scoring model  Neighborhood matching  Distance weight  Verification 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（YBGJXM（2017）06）、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
陈法敬 国家气象中心, 北京, 100081  
陈静 国家气象中心, 北京, 100081 chenj@cma.gov.cn 
韦青 国家气象中心, 北京, 100081  
李嘉鹏 浙江省气象台, 杭州, 310002  
刘凑华 国家气象中心, 北京, 100081  
杨东 山西省气象局, 太原, 030002  
赵滨 国家气象中心, 北京, 100081  
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较严重的“空报”“漏报”双重惩罚，没有考虑暴雨时空分布不均和预报评分可比性不够等问题，在分析预报员对暴雨预报评分期望值基础上，设计了一种基于可预报性的暴雨预报检验评分新方法和计算模型，分析了理想评分，并对2015—2016年4—10月中国中央气象台5 km×5 km定量降水格点预报和降水落区等级暴雨预报进行评分试验，获得了以下结果和结论：（1）预报员对暴雨预报评分期望值呈现梯级下降特征，与传统的TS评分存在显著差异；（2）设计了一种基于可预报性的暴雨预报检验新方法，通过引入e指数函数构建暴雨预报评分基函数，进而构建暴雨评分模型，该模型可以较好地拟合预报员对暴雨预报评分的期望值，同时改善了评分在不同量级阈值处的断崖式突变情况；（3）提出了预报与观测的邻域匹配方法，即一个预报点与所定义邻域中的一组观测相匹配，并利用距离加权最大值法确定暴雨评分值权重系数，预报与观测距离越近，距离权重系数越大，评分值权重越大，提高了评分的合理性，避免了距离较远的匹配站点得高分不利于鼓励预报员提高预报精度的问题；（4）对中国中央气象台逐日5 km×5 km水平分辨率的定量降水格点预报产品和中央气象台定量降水落区等级预报产品进行了评分试验，暴雨预报准确率全国平均值大于60分。基于可预报性的暴雨预报检验新评分与传统暴雨预报TS评分逐日演变特征相似，但可以较好地解析TS为0的预报评分，解析后的新评分与预报员和公众的心理预期更为接近。
英文摘要:
      In order to solve the problem that the current verification score (Threat Score, TS) for heavy rainfall forecasting severely suffers from the double punishment due to the relatively high level of missing rate and false alarm rate as well as the ignorance of the uneven temporal and spatial distribution of heavy rainfall in China, the present study designs a new verification method and computational model for heavy rainfall forecasts based on the predictability of heavy rainfall and analysis of the expectation scores of forecasters. A new score model is designed and tested using the 5 km×5 km gridded quantitative precipitation forecast and precipitation location forecast issued by the China Central Meteorological Observatory from April to October during 2015-2016. The results and conclusions are as follow. (1) Forecaster's expectation scores for heavy rainfall forecast show a staircase-like descending characteristic, which is different from the traditional TS score. (2) A new forecast verification method based on the predictability of heavy rainfall is designed, which constructs the heavy rainfall forecast score basic function by first introducing an exponential function and then constructing the heavy rainfall grading model. The model can well fit the expectation of the forecaster's score for the heavy rainfall, and improve the score by reducing its cliff-like mutation at different levels of the threshold. (3) A neighborhood method of matching forecasts and observations is proposed, that is, a forecasting point is matched with a set of observations in a defined neighborhood, and a distance-weighted maximum score method to define the weighting coefficient of the rainstorm score is used. Thereby, the closer the distance between the forecast point and the observation point is, the greater the distance-weighted coefficient is and the higher the contribution of this point to the score value is. This method improves the rationality of the score and avoids the problem that a high score from a distant matching station is not encouraging for forecasters to improve the accuracy of forecasts. (4) Quantitative gridded precipitation forecasts at the 5 km×5 km horizontal resolution and quantitative precipitation location forecasts of the China Central Meteorological Observatory are verified using this new method. The accuracy of heavy rainfall forecasts is over 60 paints on average over entire China, and the daily evolution characteristic of the new score is similar to that of the traditional TS score. However, for the forecasts whose TS scores are 0, this new score is more consistent with the psychological expectations of both the forecasters and the public.
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