Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES

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.