基于ECMWF集合预报系统降水预报的空间后处理方法及其应用效果评估

A spatial postprocessing method of precipitation forecast based on ECMWF ensemble predication system and application effect evaluation

模式后处理方法能提高定量降水预报精度,现有的基于统计的降水后处理方法多用于订正降水率或估计降水概率,忽略了降水落区预报的空间误差,导致订正效果不佳。本文提出了一种新的基于雨团匹配的空间后处理方法,用于订正降水落区预报的空间误差,从而提高预报准确率。该方法基于雨团的识别与拆分,结合贝叶斯多目标追踪法对当前时次的模式预报和实况雨团进行匹配,从而得到模式预报数据相较于实况存在的位移与强度误差,并将该误差用于随后时次模式预报数据的订正。利用该方法,对华北地区2018—2019年夏季降水过程的ECMWF集合预报系统的降水预报产品进行订正。以CMPAS中国逐小时降水分析数据作为实况值的检验结果表明,经过该方法订正后,随后时次模式降水预报的平均TS评分从0.333提高到0.369,相关系数从0.260提升到0.327,平均绝对偏差从2.788mm降到2.541mm,表明本方法能有效提高降水预报的准确率。

Modeling postprocessing methods can improve the accuracy of quantitative precipitation forecasts.At present,postprocessing methods for precipitation based on statistical analysis are mainly used to correct the precipitation rates or to estimate the precipitation proba-bility.It usually ignores the spatial displacement errors of the precipitation area forecast,thus resulting in low forecast scores.In this study,a new spatial postprocessing method based on rain cluster matching is developed to correct the spatial errors of the precipitation area forecast,in order to improve the forecasting accuracy.With the identification and separation of rain clusters,this method applies the Bayesian multi-objective tracking approach and compares the model forecasting and observed rain clusters at the current time window,so as to obtain the displacement and intensity errors between the model forecasting results and the observations.Finally,these discrepancies are used to cor-rect the model output in the coming time window.With the method proposed in this study,the precipitation forecast based on ECMWF en-semble predication system for summer precipitation processes during 2018—2019 in North China are corrected and tested.Using the CMPAS hourly precipitation analysis dataset as observations,the test results show that,after correction,the mean TS score of the precipita-tion forecasts at coming time window increases from 0.333 to 0.369,with the correlation coefficient increasing from 0.260 to 0.327,and the mean absolute error decreasing from 2.788 mm to 2.541 mm.We suggest that the method proposed in this study can effectively improve the accuracy of precipitation forecasts.