Development and Testing of the GRAPES Regional Ensemble-3DVAR Hybrid Data Assimilation System

• Published in: Acta meteorologica Sinica Vol. 29; no. 6; pp. 981 - 996
• Main Author: 陈良吕 陈静 薛纪善 夏宇
• Format: Journal Article
• Language: English
• Published: Beijing The Chinese Meteorological Society 01.12.2015; Chongqing Institute of Meteorological Sciences, Chongqing 401147; Chinese Academy of Meteorological Sciences, Beijing 100081%Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081%Chinese Academy of Meteorological Sciences,Beijing,100081%Chengdu University of Information Technology,Chengdu,610225
• Subjects: Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics and Environmental Physics; GRAPES; Meteorology; 三维变分同化; 数据同化; 测试; 混合数据; 葡萄; 误差协方差; 集合预报系统; China, People's Rep; hybrid data assimilation; regional ensemble prediction; GRAPES; GRAPES_Meso; extended control variable
• ISSN: 2095-6037; 0894-0525; 2198-0934; 2191-4788
• DOI: 10.1007/s13351-015-5021-y

Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.