Paradigms for Source Characterization
Sue Ellen Haupt, Penn State Univ., University Park, PA; and G. Young
Various groups have been devising techniques to assimilate concentration data in order to characterize the source of the contaminant. Such techniques include Bayesian approaches, Monte Carlo Markov Chain, four dimensional variational data assimilation methods, and statistical learning approaches, among others. This paper compares and contrasts the different formulations and outlines a general paradigm that encompasses all the methods. We then discuss the impact of unknown or poorly defined meteorological conditions on each of the techniques and suggest methods for characterizing sources even when those conditions are not well known or when there is a large amount of noise in the observing system. We conclude with an analysis of the prospects for accurately characterizing sources of contaminant.
Extended Abstract (500K)
Joint Session 6, Air Quality Forecasting Including Chemical Data Assimilation
Thursday, 24 January 2008, 1:30 PM-3:00 PM, 220
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