Effect of 2-m Temperature Data Assimilation in the CMA-MESO 3DVAR System  

作　　者：Zhifang XU Lin ZHANG Ruichun WANG Jiandong GONG 

机构地区：[1]CMA Earth System Modeling and Prediction Centre,China Meteorological Administration(CMA),Beijing,100081 [2]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing,100081 [3]National Meteorological Centre,China Meteorological Administration,Beijing,100081

出　　处：《Journal of Meteorological Research》2023年第2期218-233,共16页气象学报（英文版）

基　　金：Supported by the National Key Research and Development Program of China(2018YFF0300103)。

摘　　要：Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study conf

关 键 词：2-m temperature China Meteorological Administration mesoscale model(CMA-MESO) ASSIMILATION three-dimensional variational(3DVAR)data assimilation kilometer-scale 

分 类 号：P461[天文地球—大气科学及气象学]