Although "maximum-likelihood"-based data-assimilations approaches have been adopted by, and greatly benefited, global-scale and synoptic-scale NWP, their application to meso-beta and -gamma scale forecastsis limited. This is due to the fact that small-scale weather processes vary greatly in time and space,and that observations available for such scales are relatively sparse and are insufficient to define the local-scale circulations. In the last five years, NCAR, in collaboration with the Army Test and Evaluation Command (ATEC), has developed a regional four-dimensional data assimilation (FDDA) and forecasting system based on a continuous "observation nudging" approach. This approach is able to effectively combine the full-physics Penn State/NCAR MM5 model, with fine-scale underlying soil forcing, and all available synoptic and asynoptic observations, where the goal is to provide the best-possible analysis and short-term forecasts. Recently, this data-assimilation approach has been implemented in the Weather Research and Forecast (WRF) model, to facilitate migration of the ATEC FDDA systems from MM5 to WRF. TheWRF-FDDA system will also be included in the community WRF system, for general use by the research community. In this paper, the design and major features of the WRF-FDDA system are introduced. Examples ofthe application of WRF-FDDA for mesoscale data analysis and forecasting in support of ATEC and for high-impact weather events such as the 2005 hurricanes, are also presented.