In this final year of a 3 year effort by the Naval Research Laboratory Code 7544 to enable Navy aerosol forecasting to take full advantage of available data feeds through the investigation of fundamental atmospheric composition observability, we find that the next generation of data feeds and associated technology present a number of challenges and opportunities which require attention. These include: 1) The transition from NASA EOS sensors to the next generation of diversified operational and near real time data sources; 2) The move to a constellation approach for global atmospheric composition observing, 3) The expected near real time availability of US and European lidar data; 4) The enhanced availability of surface and aircraft observations; and 5) Increased aerosol model demands for such applications as joint surface-atmosphere retrievals, directed energy (DE), and intelligence, surveillance, and reconnaissance (ISR). This increase in the number of potential data sources, coupled with further efficacy demands, creates an imperative need to understand the nature of global aerosol observability and the development of realistic uncertainties for composition observations, retrievals and model products. Outstanding problems facing the community relate to such issues as observation bias, representativeness, and information spreading for the myriad of sporadic data sources available. To meet this need, in this grant we investigated the use of these diverse flows of data using ensemble and ensemble data assimilation technologies to be incorporated into the Navy Aerosol Analysis and Prediction System (NAAPS)/Navy Variational Analysis Data Assimilation-Aerosol Optical Depth (NAVDAS-AOD) framework. This work also led to the development of the world s first quasi-operational global multi-model ensemble, drawing from the world s operational data centers. This is the nucleus of a potential operational multi-model ensemble.