A Fast Running Test Bed Model to Evaluate Atmospheric Plume Source Properties I: Initial Test Scenario

Abstract

Given an unknown but detected release of a toxic agent, the current NARAC capability for reconstructing source characteristics is a highly manual procedure that often relies on analyst judgement and requires many hours of computations for a refined analysis. There is no automated, optimization approach to estimating the source characteristics. A fast running, prototype atmospheric inversion model has been developed for use as a test bed for the evaluation of source inversion schemes. The model was applied to a simple puff release scenario to test the relationship between the amount of sampled data obtained and the accuracy of the determination of the inverted source parameters. The initial inversion scheme chosen for the test bed model utilizes the Marquardt method coupled to a Gaussian puff atmospheric dispersion model driven by a COAMPS model wind field. The inversion scheme results are used in conjunction with a sensor realization probability model for a sensor realization scenario consisting of lo00 possible sensor realizations. The results of the initial test calculations indicate that the inversion procedure produces good results for the four source parameters, location (x, y), release time, and strength along with reasonably well defined maximum probabilities for the sensor realization scenarios. The model runs relatively fast, taking -100 seconds per inversion on a Sparc 10 workstation.

A Fast Running Test Bed Model to Evaluate Atmospheric Plume Source Properties II: Source Location Inversion Sensitivity Tests

“…A recent report (Grossman et al, 2001, "GMG') described a technique in which selected source properties for a toxic material released into the atmosphere are obtained by inversion of a set of measured concentrations. The technique utilizes the Marquardt inversion method coupled to a Gaussian puff atmospheric dispersion model (mtegrated PUFF, Petersen and Lavdas, 1986) driven by a COAMPS regional prognostic model (Hodur, 1997) wind field.…”