不同初始场条件对新疆区域数值模式预报性能的影响

Effect of Different Initial Fields Conditions on the Prediction Performance of Regional Numerical Model in Xinjiang

基于GRAPES_GFS和NCEP_GFS两种初始场,详细比较了两者之间的差异,分别利用两种初始场驱动新疆区域数值模式(RMAPS-CA V1.0),对2021年4月的数值预报结果以及2021年4月21日一次暴雨过程模拟结果进行MET(Model Evaluation Tools)对比检验。结果表明:(1)两种资料的位势高度扰动场、温度扰动场、湿度扰动场存在明显差异,其相关系数分别为0.26~0.60、0.05~0.24和0.01~0.12,导致在次天气尺度上存在着较大差异,并由此造成了模拟结果之间的差异,反映了区域模式对初始场和边界条件的敏感性;(2)从高空位势高度、风速、温度的预报结果看,NCEP_GFS初始场在新疆区域模式中高空要素的预报效果均要优于GRAPES_GFS初始场,均方根误差分别降低35.5%~37.2%、7.6%~12.6%和6.0%~17.2%。从地面常规预报量的检验看,GRAPE_GFS初始场对2 m温度和10 m风速的预报效果优于NCEP_GFS初始场,均方根误差分别降低14.3%和6.8%;(3)从降水检验评分看,两种初始场的降水预报整体为漏报现象,NCEP_GFS初始场针对各降水阈值及不同时效的预报降水评分高于GRAPES_GFS,0.1、6.1和12.1 mm/6 h的TS评分分别提高22.5%、16.1%和150.8%;(4)从一次暴雨过程预报的检验结果看,GRAPES_GFS对于24 h为小量级降水预报效果优于NCEP_GFS,准确率分别为61.4%和40.0%;而NCEP_GFS对于大量级的降水预报则优于GRAPES_GFS,准确率分别为66.7%和33.3%。两种初始场对降水个例检验的偏差以空报现象为主,NCEP_GFS的TS评分整体高于GRAPES_GFS。

The initial field conditions directly affect the predictive performance of the regional model.Based on the two initial fields of GRAPE_GFS and NCEP_GFS,the differences between them were compared in detail,and then the two initial fields were used to drive the Xinjiang regional numerical model(RMAPS-CA V1.0),the numerical simulation results of the full month of April 2021 and the simulation results of a rainfall process on April 21,2021 were compared and tested by MET(model evaluation tools).The results showed that:firstly,there are obvious differences in the height disturbance field,temperature disturbance field and humidity disturbance field between the two kinds of data,with correlation coefficients of 0.26~0.60,0.05~0.24,and 0.01~0.12,respectively,leading to great differences one sub-synoptic scales and thus causing the differences between simulation results,reflecting the sensitivity of the regional model to initial fields and boundary conditions.Secondly,the prediction effect of NCEP_GFS initial field is better than that of GRAPES_GFS initial field in the Xinjiang regional model,and the RMSE(root mean square error)decrease by 35.5%~37.2%,7.6%~12.6%and 6.0%~17.2%,respectively.The results show that GRAPE_GFS initial field are better than NCEP_GFS initial field in predicting temperature at 2 m and wind speed at 10 m,the RMSE decrease by 14.3%and 6.8%,respectively.Thirdly,from the precipitation threat score(TS),it can be seen that the precipitation forecasts of the two initial fields are generally underreported,and the TS of the NCEP_GFS initial field is higher than that of GRAPES_GFS for different precipitation’s thresholds and different timings,and the TS of 0.1 mm/6 h,6.1 mm/6 h and 12.1 mm/6 h increase by 22.5%,16.1%and 150.8%,respectively.Finally,according to the test results of a heavy rainfall process forecast,GRAPES_GFS is better than NCEP_GFS for 24-hours of small-scale precipitation,with accuracy rates of 61.4%and 40.0%,respectively.However,NCEP_GFS is better than GRAPES_GFS for large-scale precipitation forecasting,with accuracy rates of 66.7%and 33.3%,respectively.The BIAS of two initial fields on precipitation cases are mainly due to the phenomenon of empty reporting,and the TS score of NCEP_GFS is higher than that of GRAPES_GFS on the whole.