A nested version of the NRL Atmospheric Variational Data Assimilation System (NAVDAS) has been developed for use with the Navy’s mesoscale numerical weather prediction model COAMPS. Data innovations (ob-background) are obtained by successively interpolating the 6-hour COAMPS forecast fields to the data locations and subtracting the result from the observations for each of the successively finer grid meshes. At the end of the procedure each observation innovation is computed from the highest resolution background forecast available. In a boundary region around the coarse grid mesh, the Navy’s global model NOGAPS forecasts are used to generate data innovations for observations outside the COAMPS domain, allieviating the need for psuedo-observations to maintain continuity across the COAMPS boundaries. An analysis grid is constructed by removing any duplicate grid points from the COAMPS grid meshes. Over the COAMPS domain, a larger number of the observations are used in the analysis compared to the global version of NAVDAS running with NOGAPS. For the analysis in regions with higher resolution background fields, smaller correlation length scales can be used for the background error covariance. Following the analysis, the background fields on the coarser meshes are first updated by averaging values on the inner meshes and the analysis corrections then added to each backgound grid. Statistical comparsions of the performance of the nested version of NAVDAS and the current operational MVOI analysis system for data assimilation with the COAMPS model will be presented.