Advances in Engineering Software - Special issue on large-scale analysis, design and intelligent synthesis environments
Algorithm 247: Radical-inverse quasi-random point sequence
Communications of the ACM
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
SIAM Journal on Scientific Computing
A stochastic projection method for fluid flow II.: random process
Journal of Computational Physics
Adjoint sensitivity analysis of regional air quality models
Journal of Computational Physics
An Equation-Free, Multiscale Approach to Uncertainty Quantification
Computing in Science and Engineering
Numerical study of uncertainty quantification techniques for implicit stiff systems
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Uncertainty propagation in puff-based dispersion models using polynomial chaos
Environmental Modelling & Software
Environmental Modelling & Software
Managing uncertainty in integrated environmental modelling: The UncertWeb framework
Environmental Modelling & Software
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Current air quality models generate deterministic forecasts by assuming perfect model, perfectly known parameters, and exact input data. However, our knowledge of the physics is imperfect. It is of interest to extend the deterministic simulation results with ''error bars'' that quantify the degree of uncertainty, and analyze the impact of the uncertainty input on the simulation results. This added information provides a confidence level for the forecast results. Monte Carlo (MC) method is a popular approach for air quality model uncertainty analysis, but it converges slowly. This work discusses the polynomial chaos (PC) method that is more suitable for uncertainty quantification (UQ) in large-scale models. We propose a new approach for uncertainty apportionment (UA), i.e., we develop a PC approach to attribute the uncertainties in model results to different uncertainty inputs. The UQ and UA techniques are implemented in the Sulfur Transport Eulerian Model (STEM-III). A typical scenario of air pollution in the northeast region of the USA is considered. The UQ and UA results allow us to assess the combined effects of different input uncertainties on the forecast uncertainty. They also enable to quantify the contribution of input uncertainties to the uncertainty in the predicted ozone and PAN concentrations.