Stochastic finite elements: a spectral approach
Stochastic finite elements: a spectral approach
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
SIAM Journal on Scientific Computing
Numerical study of uncertainty quantification techniques for implicit stiff systems
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
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Effective environmental protection policy making depends on comprehensive and accurate Air Quality Model (AQM) prediction results. The confidence level associated with the model prediction, as well as the uncertainty sources that contribute to the prediction uncertainty are important information that should not be neglected when interpreting simulation results. In this work, we explore the capability of the polynomial chaos (PC) method for uncertainty quantification (UQ) and propose a uncertainty apportionment (UA) approach that can be easily applied to any forecast models. The numerical tests on the STEM (Sulfur Transport Eulerian Model) for the northeast region of the United States provide a categorization for the major uncertainty sources that contribute to the uncertainty in the ozone concentration prediction. This information can be used to guide the optimal investment decisions as to which input measurement accuracy should be improved to make the maximum impact on reducing the uncertainty in the prediction result.