Uncertainty apportionment for air quality forecast models

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Uncertainty apportionment for air quality forecast models

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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents

Honolulu, Hawaii

SESSION: Computational sciences track table of contents

Pages 956-960

Year of Publication: 2009

ISBN:978-1-60558-166-8

Authors

Haiyan Cheng	Virginia Polytechnic Institute and State University, Blacksburg, Virginia
Adrian Sandu	Virginia Polytechnic Institute and State University, Blacksburg, Virginia

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

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