TY - JOUR
T1 - Monte Carlo source detection of atmospheric emissions and error functions analysis
AU - Cervone, Guido
AU - Franzese, Pasquale
N1 - Funding Information:
This material is partly based upon work supported by the National Science Foundation under Grant no. AGS 0849191.
PY - 2010/7
Y1 - 2010/7
N2 - A Monte Carlo algorithm is iteratively run to identify candidate sources for atmospheric releases. The values of the ground measurements of concentration are synthetically generated by a benchmark simulation of a Gaussian dispersion model. At each iteration, a Gaussian reflected plume model is applied to compute the dispersion from a candidate source, and the resulting concentrations are compared with the measurements at fixed points on the ground. Iterative algorithms for detection of atmospheric release sources are based on the optimization of an error function between numerical simulations and observations. However, the definition of error between observations and simulations by an atmospheric dispersion model is not univocal. In this paper, the comparisons are made using various error functions. The characteristics of different error functions between model predictions and sensor measurements are investigated, with a statistical analysis of the results. Sensitivity to domain size and addition of random noise to the measurements are also investigated.
AB - A Monte Carlo algorithm is iteratively run to identify candidate sources for atmospheric releases. The values of the ground measurements of concentration are synthetically generated by a benchmark simulation of a Gaussian dispersion model. At each iteration, a Gaussian reflected plume model is applied to compute the dispersion from a candidate source, and the resulting concentrations are compared with the measurements at fixed points on the ground. Iterative algorithms for detection of atmospheric release sources are based on the optimization of an error function between numerical simulations and observations. However, the definition of error between observations and simulations by an atmospheric dispersion model is not univocal. In this paper, the comparisons are made using various error functions. The characteristics of different error functions between model predictions and sensor measurements are investigated, with a statistical analysis of the results. Sensitivity to domain size and addition of random noise to the measurements are also investigated.
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U2 - 10.1016/j.cageo.2010.01.007
DO - 10.1016/j.cageo.2010.01.007
M3 - Article
AN - SCOPUS:77953543708
SN - 0098-3004
VL - 36
SP - 902
EP - 909
JO - Computers and Geosciences
JF - Computers and Geosciences
IS - 7
ER -