Extended Abstract (1.4M)5B.2
4-Dimensional Variational (4D-Var) data assimilation for the Weather Research and Forecasting model (WRF) model
Xiang-Yu Huang, NCAR, Boulder, CO; and Q. Xiao, X. Zhang, W. Huang, D. M. Barker, J. Michalakes, J. Bray, Z. Ma, T. Henderson, J. Dudhia, X. Zhang, D. J. Won, Y. R. Guo, H. C. Lin, and Y. H. Kuo
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.
Session 5B, WRF: System development
Wednesday, 27 June 2007, 8:00 AM-10:00 AM, Summit B
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