Automated source parameter estimation for atmospheric transport and dispersion applications

Accurate simulations of atmospheric transport and dispersion rely heavily on the source term parameters necessary to characterize the initial release. These source parameters are in many cases not known and consequently based on rudimentary assumptions. Here we will describe a computationally efficient system that combines backward trajectory and variational data assimilation techniques to characterize the release source parameters and provide a refined hazard assessment, using available observations. The underlying algorithm consists of a combination of modelling systems, including the Second order Closure Integrated PUFF model (SCIPUFF), its corresponding Source Term Estimation (STE) model, a Hybrid - Lagrangian-Eulerian Plume Model (H-LEPM), its numerical adjoint, and the software infrastructure necessary to link them. SCIPUFF and its STE model are used to calculate a "first guess" source estimate based on the available chemical plume and meteorological observations. The H-LEPM and corresponding adjoint are then used to iteratively refine the SCIPUFF based STE estimate using variational data assimilation techniques. The entire process from beginning to end is completely automated and requires no human intervention. 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). An end-toend prototype of this system has been developed to illustrate how it could potentially be deployed within the United States (US) Joint Effects Model (JEM). Preliminary results suggest that this concept provides an efficient means to better utilize CB and meteorological observations to provide a more accurate hazard assessment.