Preparing input data for sensitivity analysis of an air pollution model by using high-performance supercomputers and algorithms

摘要

Sensitivity analysis is an important issue in large-scale mathematical modelling. We developed a novel 3-stage method for global sensitivity analysis of the Unified Danish Eulerian Model (UNI-DEM). This is a powerful large-scale air pollution model with an up-to-date high-performance software implementation. There is a number of uncertain internal parameters, especially in the chemistry-emission submodel, which are subject to our quantitative sensitivity study. Efficient Monte Carlo and quasi-Monte Carlo algorithms based on Sobol sequences are used in this study.