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Accurate aerosol and visibility forecasts are critical to both civilian and military applications. To improve the Navy's electro-optical propagation forecast capability, the world's first operational aerosol assimilation model was developed for improving the short and long-term forecasts of NAAPS through the assimilation of the over-water MODIS aerosol products. In this project, the capability of over-land aerosol data assimilation was studied, a multi-sensor data assimilation technique was evaluated, and a prototype of a system that assimilates MODIS fine model aerosol optical depth ratio was developed for improving model representation of aerosol speciation. Extending beyond the scope of this study, a 3-D aerosol assimilation package was constructed, which is designed to improve the accuracy of NAAPS aerosol vertical profiles by assimilating data from CALIPSO. With knowledge gained from the multi-sensor analysis, the long-term aerosol trend was also studied for climate related applications. This research project yielded a total often authored or coauthored peer-reviewed journal papers and more than twenty conference presentations.

This report provides a technical description of the adjoint code for the NRL Atmospheric Variational Data Assimilation System (NAVDAS). The output of the NAVDAS adjoint is an array that contains the sensitivity of a user-defined forecast cost function with respect to the complete set of observations assimilated by NAVDAS at a particular time. The input for the NAVDAS adjoint is an analysis sensitivity vector provided by the adjoint of a forecast model such as NOGAPS. Step by step guidelines for using the NAVDAS adjoint system to compute observation sensitivity are given. The basic theory of observation sensitivity is outlined, and features of the adjoint code and main subroutines are described in detail. Examples of observation sensitivity applications are provided, including the impact of innovations on short-range forecast error, and targeted observing.

Remotely-sensed observations from satellites hold great promise for providing a wealth of high-temporal and spatial resolution data, particularly over the oceans. However, it has been very difficult for numerical weather prediction systems to show consistent, positive forecast improvement attributable to satellite retrievals obtained in the conventional manner. By developing techniques for the direct assimilation of the satellite measured radiances, this project will lay the foundation for improved three- and four-dimensional data assimilation systems for global and mesoscale applications. These systems may be run at operational central-site forecast centers or run on-scene to provide operational mission support.