Fusion of chemical, biological, and meteorological observations for agent source term estimation and hazard refinement

Chemical and biological (CB) agent detection and effective use of these observations in hazard assessment models are key elements of our nation's CB defense program that seeks to ensure that Department of Defense (DoD) operations are minimally affected by a CB attack. Accurate hazard assessments rely heavily on the source term parameters necessary to characterize the release in the transport and dispersion (T&D) simulation. Unfortunately, these source parameters are often not known and based on rudimentary assumptions. In this presentation we describe an algorithm that utilizes variational data assimilation techniques to fuse CB and meteorological observations to characterize agent release source parameters and provide a refined hazard assessment. The underlying algorithm consists of a combination of modeling systems, including the Second order Closure Integrated PUFF model (SCIPUFF), its corresponding Source Term Estimation (STE) model, a hybrid Lagrangian-Eulerian Plume Model (LEPM), its formal adjoint, and the software infrastructure necessary to link them. SCIPUFF and its STE model are used to calculate a "first guess" source estimate. The LEPM and corresponding adjoint are then used to iteratively refine this release source estimate using variational data assimilation techniques. This algorithm has undergone preliminary testing using virtual "single realization" plume release data sets from the Virtual THreat Response Emulation and Analysis Testbed (VTHREAT) and data from the FUSION Field Trials 2007 (FFT07). The end-to-end prototype of this system that has been developed to illustrate its use within the United States (US) Joint Effects Model (JEM) will be demonstrated.