Uncertainty propagation in puff-based dispersion models using polynomial chaos

Umamaheswara Konda, Tarunraj Singh, Puneet Singla, Peter Scott

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.

Original languageEnglish (US)
Pages (from-to)1608-1618
Number of pages11
JournalEnvironmental Modelling and Software
Volume25
Issue number12
DOIs
StatePublished - Dec 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Environmental Engineering
  • Ecological Modeling

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