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

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. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A vast number of numerical experiments were carried out with SA-DEM on an IBM Blue Gene/P, the most powerful parallel supercomputer, at the time of the write-up of this paper, in Bulgaria. Even this powerful machine has some problems with the storage when SA-DEM is to be run with the refined (480×480) version of the mesh. The code was implemented with some enhancements on the IBM MareNostrum III at BSC — Barcelona, the most powerful parallel supercomputer in Spain. This implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results and performance analysis of these results will be presented in the paper. [ABSTRACT FROM AUTHOR]