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Dale Barker, Xiang-Yu Huang, Zhiquan Liu, Tom Auligné, Xin Zhang, Steven Rugg, Raji Ajjaji, Al Bourgeois, John Bray, Yongsheng Chen, Meral Demirtas, Yong-Run Guo, Tom Henderson, Wei Huang, Hui-Chuan Lin, John Michalakes, Syed Rizvi, and Xiaoyan Zhang

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

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Xiang-Yu Huang, Qingnong Xiao, Dale M. Barker, Xin Zhang, John Michalakes, Wei Huang, Tom Henderson, John Bray, Yongsheng Chen, Zaizhong Ma, Jimy Dudhia, Yongrun Guo, Xiaoyan Zhang, Duk-Jin Won, Hui-Chuan Lin, and Ying-Hwa Kuo


The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.

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