Comparative evaluation of computationally efficient uncertainty propagation methods through application to regional-scale air quality models

This work presents the comparative evaluation of two computationally efficient uncertainty propagation techniques: the Stochastic Response Surface Method (SRSM) and the High Dimensional Model Representation (HDMR) method. The evaluation is performed in relation to the applicability to these methods to complex numerical models, specifically those dealing with simulating regional-scale air quality. The air quality model used in the application case study is a Eulerian type three-dimensional grid-based model, and involves a large set of non-linear partial and ordinary differential equations to describe atmospheric transport and chemistry, thus making it impractical to use traditional Monte Carlo based techniques for performing uncertainty analysis. The application case study focuses on studying uncertainties in ozone levels estimated by a regulatory air quality model due to uncertainties in biogenic emissions of ozone precursors. Preliminary results show that 95th confidence interval for the peak ozone levels spans a range of over ± 15% from the mean value, indicating significant uncertainties with respect to the health impact and regulatory compliance. Both the SRSM and HDMR methods provide similar estimates, thus serving to cross-validate each other, while requiring a small number of model simulations.