4-Dimensional Variational (4D-Var) data assimilation for the Weather Research and Forecasting model (WRF) model

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) Use observations at the almost exact times (to the width of the observation windows) that they are observed, which suits most asynoptic data, 2) Implicitly use flow-dependent background errors, which ensures the analysis quality for fast developing weather systems, and 3) Use a forecast model as a constraint, which ensures the dynamic balance of the final analysis.

The last mentioned advantage also implies 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 4D-Var component of WRF-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 the 4D-Var system. Preliminary results indicate that the 4D-Var works properly and can be used to assimilate many observations of different types.