Monte Carlo source detection of atmospheric emissions and error functions analysis

Guido Cervone, Pasquale Franzese

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)902-909
Number of pages8
JournalComputers and Geosciences
Volume36
Issue number7
DOIs
StatePublished - Jul 1 2010

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

Cite this