苏翔,袁慧玲,朱跃建. 2020. 四种定量降水预报客观订正方法对比研究[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.071
四种定量降水预报客观订正方法对比研究
Comparison study of four objective quantitative precipitation forecast calibration methods
投稿时间:2020-04-03  修订日期:2020-07-15
DOI:10.11676/qxxb2020.071
中文关键词:  频率匹配法,最优TS评分,最优百分位,概率匹配,定量降水预报,偏差订正
英文关键词:Frequency matching method, Optimal threat score, Optimal percentile, Probability matching, Quantitative precipitation forecast, Bias correction
基金项目:国家自然科学基金, 中国气象局预报员专项, 国防预研项目
作者单位E-mail
苏翔 江苏省气象台, 中国气象局交通气象重点开放实验室 suxiang01@163.com 
袁慧玲 南京大学大气科学学院与中尺度灾害性天气教育部重点实验室 yuanhl@nju.edu.cn 
朱跃建 美国国家环境预报中心/环境模拟中心 yuejian.zhu@noaa.gov 
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中文摘要:
      基于2019年全年、不同季节、不同预报时效的ECMWF模式定量降水预报(QPF),检验评估了频率匹配(FMM)、最优TS评分(OTS)、最优百分位(OP)、概率匹配(PM)四种QPF客观订正方法的综合性能。利用理想模型研究了不同雨带位移偏差和干湿偏差情形下FMM和OTS的表现,并通过个例订正展示了四种QPF订正方法的基本特征。结果表明:FMM与OTS仅能对确定性预报的降水量级进行调整,OP和PM方法通过引入集合预报信息可在一定程度上改变预报的降水落区形态。FMM以频率偏差最优为目标,可以很好地消除模式的干湿偏差,但仅在位移偏差较小且存在较大干湿偏差时提升原始预报的TS评分。OTS方法难以改进存在弱湿偏差的中雨预报的TS评分,而OP方法利用集合预报信息可以显著提升所有降水等级的TS评分,在较长预报时效下优势尤其明显,但也存在春夏两季湿偏差较大的问题。PM由于没有使用历史实况信息,在暴雨订正中干偏差较大。经济价值模型检验评估表明,OP在暴雨量级的风险决策中具有较高的参考价值。
英文摘要:
      Four objective quantitative precipitation forecast (QPF) calibration methods, including frequency matching method (FMM), optimal threat score (OTS), optimal percentile (OP) and probability matching (PM), were comprehensively verified based on the annual and seasonal ECMWF QPFs at different lead times. An ideal model was proposed to study the performance of FMM and OTS under different scenarios of spatial displacement and dry/wet biases. A heavy rain case was used to demonstrate the basic characteristics of the four different calibration methods. Results showed that FMM and OTS can only adjust the magnitude of deterministic QPF, while OP and PM can change the pattern of QPF to some extent by using ensemble forecast information. Aiming at optimizing the frequency bias, FMM can eliminate the dry/wet bias of QPFs well, but it can only improve the threat score (TS) of original QPFs when the displacement error is small and the dry/wet bias is large. OTS has limited skill in improving the TS of moderate rain with the weak wet bias. By contrast, OP can improve the TS of all precipitation thresholds, benefiting from using ensemble forecast information, especially for longer forecast lead times. However, OP shows large wet biases during spring and summer seasons, while PM suffers from large dry biases for torrential rain events due to the lack of historical observation information. The evaluation of economic value model showed that OP has relatively higher reference value for torrential rain in risk decision making.
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