The scientific design and preliminary results of the data assimilation component of the Global-Regional Prediction and Assimilation System (GRAPES) recently developed in China Meteorological Administration (CMA) are presented in this paper. This is a three-dimensional variational (3DVar) assimilation system set up on global and regional grid meshes favorable for direct assimilation of the space-based remote sensing data and matching the frame work of the prediction model GRAPES. The state variables are assumed to decompose balanced and unbalanced components. By introducing a simple transformation from the state variables to the control variables with a recursive or spectral filter, the convergence rate of iteration for minimization of the cost function in 3DVar is greatly accelerated. The definition of dynamical balance depends on the characteristic scale of the circulation considered. The ratio of the balanced to the unbalanced parts is controlled by the prescribed statistics of background errors. Idealized trials produce the same results as the analytic solution. The results of real data case studies show the capability of the system to improve analysis compared to the traditional schemes. Finally, further development of the system is discussed.
Constructing βmesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction （NWP） model. Without vertical velocity matching the βmesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrom eteors. In this study, a method, basing on the threedimensional variational （3DVAR） assimilation technique, was developed to obtain reasonable structures of βmesoscale weather systems by assimilating radar data in a nextgeneration NWP system named GRAPES （the Global and Regional Assimilation and Prediction System） of China. Singlepoint testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity （taking Richardson’s equation as the observational operator） can greatly improve the vertical motion. Ex periments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further indepth study.
