摘要
将C波段雷达资料用LAPS模式的云分析系统进行反演,并采用Nudging技术将反演得到的云微物理场引入GRAPES中尺度数值模式,结合1次强降水天气过程的模拟实验,研究了C波段雷达资料同化对GRAPES中尺度数值模式短临降水预报的影响。结果表明:(1)雷达资料同化能够改进中尺度模式的降水预报,模式前6 h的降水预报相关系数和不同等级降水TS评分都有提高,同时降水峰值提前了1 h,有助于缓解模式spin-up问题。(2)模式降水预报的改进效果主要由强降水贡献,最大改进效果集中在4~6 h。(3)同化雷达资料后25 mm以上强降水预报站数比更接近实况,落区偏差幅度更小,降水落区和强度向实况方向得到调整。(4)10 mm以下弱降水在吸收雷达资料后,站数比相较于控制预报,比实况增加更多,落区偏差幅度增大,存在预报过量的问题。弱降水预报过量主要集中在4~6 h,而前3 h对降水预报的改进有积极作用。
The C-band radar data is inverted with the LAPS model cloud analysis system,and the cloud microphysical field obtained from the inversion is introduced into the GRAPES mesoscale numerical model using Nudging technology,combined with a simulation experiment of a heavy precipitation weather process to study the influence of C-band radar data assimilation on the short-term precipitation forecast of GRAPES mesoscale numerical model.The results show that:①Radar data assimilation can improve the precipitation forecast of the mesoscale model.The precipitation forecast correlation coefficient and TS scores of different grades of precipitation in the first 6 hours of the model have been improved.At the same time,the precipitation peak is advanced by 1 hour,which helps to alleviate the model spin-up problem.②The improvement effect of model precipitation forecast is mainly contributed by heavy precipitation,and the maximum improvement effect is concentrated in 4~6 hours.③After assimilating the radar data,the number of forecasting stations for heavy precipitation above 25 mm is closer to the actual situation,the deviation of the falling area is smaller,and the precipitation falling area and intensity are adjusted to the actual direction.④After absorbing radar data for weak precipitation below 10 mm,the number of stations comparison control forecast increased more than the actual situation,the deviation range of the landing area increased,and there was a problem of excessive forecasting.The weak precipitation forecast is mainly concentrated in 4~6 hours,and the first 3 hours have a positive effect on the improvement of precipitation forecast.
作者
齐大鹏
杨静
李彦霖
周明飞
朱文达
QI Dapeng;YANG Jing;LI Yanlin;ZHOU Mingfei;ZHU Wenda(Guizhou Meteorological Observatory,Guiyang 550002,China)
出处
《中低纬山地气象》
2022年第3期25-31,共7页
Mid-low Latitude Mountain Meteorology
基金
贵州省气象局科研业务项目(黔气科登[2021]05-01号):贵州C波段雷达资料在GRAPES中尺度数值模式中的同化研究。
