Automated source parameter and low level wind estimation for atmospheric transport and dispersion applications

Accurate simulations of the atmospheric transport and dispersion rely heavily on the source term parameters necessary to characterize the initial release and meteorological conditions that drive the downwind dispersion. The source parameters are in many cases not known and consequently based on rudimentary assumptions. This is particularly true of accidental releases, and the intentional releases associated with terrorist incidents. Often the available meteorological observations are not representative of the conditions at the location of the release, which can result in significant errors in the hazard assessments downwind of the sensors even when the source parameters are accurately characterized. In this presentation we describe a computationally efficient algorithm that utilizes variational data assimilation techniques to produce a refined downwind hazard assessment by using all available observations to characterizing the release source parameters and the low-level winds. 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 based on the available chemical plume and meteorological observations. The 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 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-to-end prototype of this system has been developed to illustrate how it could potentially be deployed within the United States (US) Joint Effects Model (JEM). The STE prototype will be demonstrated in this presentation using VTHREAT generated chemical observations from point chemical detection systems. Preliminary results suggest that this concept provides an efficient means to better utilize CB and meteorological observations to provide a more accurate hazard assessment.