87th AMS Annual Meeting

Tuesday, 16 January 2007: 8:45 AM
Characterizing Contaminant Source and Meteorological Forcing using Data Assimilation with a Genetic Algorithm
210B (Henry B. Gonzalez Convention Center)
Kerrie J. Long, Penn State Univ., University Park, PA; and S. E. Haupt, G. Young, and C. T. Allen
Poster PDF (324.7 kB)
In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Unfortunately, meteorological data often have insufficient spatial and temporal resolution for precise modeling of pollutant dispersion and is therefore inaccurate or unrepresentative. This issue is addressed via a method that simultaneously tunes the surface wind and the pollutant source characteristics. This method uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind that best matches monitored receptor data with pollutant dispersion model output. The approach is validated using synthetic receptor data generated by the Gaussian plume and puff equations. Given sufficient receptor data, the GA is able to reproduce the synthetically generated wind, source location, and source strength. The minimum requirements for data quantity and quality are determined by sensitivity analysis.

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