The 4D-Var idea has been pursued actively by research community and operational centers over the past two decades. The 4D-Var technique has a number of advantages including the abilities to: 1) Implicitly use flow-dependent background errors, which ensures the analysis quality for fast developing weather systems, and 2) Use a forecast model as a constraint, which enhances the dynamic balance of the final analysis. These advantages suggest that the community WRF-Var system should be enhanced by complementing the current 3D- to a 4-dimensional capability, using the WRF forecast model as a constraint, in order to provide the best initial conditions for the WRF model. The WRF 4D-Var has been under extensive development since 2004. The prototype 4D-Var was built in 2005 and has under continuous refinement since then. Many single observation experiments have been carried out to validate the correctness of the 4D-Var formulation. A series of real data experiments have been conducted to assess the meteorological performance of WRF 4D-Var.
Another year of fast development of WRF-Var (4D) leads to its basic system (version 2.2). It has the following features:
         It runs as a combination of WRF (the released version 2.2), WRF+ (the WRF tangent linear model and adjoint model) and WRF-Var (the release version 2.1 with 4D-Var extensions) executables,
         It uses calls to “system” to invoke the three executables,
         It uses disk I/O to handle the communication among WRF, WRF+ and WRFVAR,
         It can run on a single processor and also multi-processors,
         It has a penalty term, Jc, to control noise during the minimization, and
         It includes a simple vertical diffusion scheme and a large-scale condensation scheme in WRF+.
The parallel MPMD system architecture of WRF-Var (4D) has demonstrated encouraging performance and made cycling data assimilation experiments possible. In the seminar, I will review what we have done so far, what we have accomplished and what we have planned for the coming years.