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.