Improvements and Performances of the Operational GRAPES_GFS 3DVar System

In recent years, the capability and stability of GRAPES (Global/Regional Assimilation and PrEdiction System) three-dimensional variation data assimilation system (3DVar) is upgraded and improved gradually in Numerical Weather Prediction Center of China Meteorological Administration. Improvements in analysis scheme and assimilating data technique for GRAPES 3DVar in the past two years are overviewed. Then the capability and performance of G-M3DVar latest version are evaluated by two-year length experiments. The accuracy and precision of G-M3DVar analyses is evaluated against radiosonde observation and ERA-Interim reanalysis and is compared with NCEP FNL and T639 analysis.Taken radiosonde data as a reference, the root mean square error and bias of pressure analyses of G-M3DVar are smaller than ERA-Interim reanalysis and NCEP FNL analysis data in all domains in both winter and summer seasons. The root mean square error and bias of u wind analysis of G-M3DVar are larger than ERA-Interim reanalysis and NCEP FNL analysis in the Tropics. However, in the Northern Hemisphere, the root mean square error and bias of u wind of G-M3DVar are similar to ERA-Interim below 250 hPa. In the Southern Hemisphere, the root mean squared error of u wind of G-M3DVar is the largest compared to EAR-Interim reanalysis and NCEP FNL analysis. For humidity field, the bias of G-M3DVar analysis is smaller than EAR-Interim reanalysis and NCEP FNL analysis in the middle and high troposphere, which means that the humidity analysis of G-M3DVar is much drier than ERA-Interim and NCEP FNL data especially in the middle and high troposphere. Taken ERA-Interim reanalysis data as a reference, the root mean square error of G-M3DVar analysis is smaller than the T639 analysis but larger than NCEP FNL analysis data for all fields excluded the humidity.In conclusion, the quality of G-M3DVar analysis is better than T639 analysis and satisfies requirements of operational run. In recent years, the gap of analyses between G-M3DVar and advanced numerical weather centers such as ECMWF keeps growing, although the accuracy of G-M3DVar analysis is improved significantly in the past two years. Much more focus and works should be paid in the following aspects. First, the background error covariance (BE) is estimated by National Meteorological Center of USA (NMC) method, which is static and climatological. The static and climatological BE is far from meeting requirements of the modern numerical weather prediction. Second, the quality control scheme for all observations in G-M3DVar is still relatively inexactly and incapable. Third, the bias correction scheme for microwave radiance in G-M3DVar is still static which has been proved to have some shortcomings.