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

Sastry S. Isukapalli, Alper Unal, Sheng Wei Wang, Panos G. Georgopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationCollection of Technical Papers - 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
Pages1203-1210
Number of pages8
ISBN (Print)1563478234, 9781563478239
DOIs
StatePublished - 2006
Event11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference - Portsmouth, VA, United States
Duration: Sep 6 2006Sep 8 2006

Publication series

NameCollection of Technical Papers - 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
Volume2

Other

Other11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
Country/TerritoryUnited States
CityPortsmouth, VA
Period9/6/069/8/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Comparative evaluation of computationally efficient uncertainty propagation methods through application to regional-scale air quality models'. Together they form a unique fingerprint.

Cite this