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RECORD NUMBER: 21 OF 25

OLS Field Name OLS Field Data
Main Title Sensitivity and Uncertainty Analyses for Numerical Advection Processes.
Author Hwang, D. ; Byun, D. W. ;
CORP Author MCNC, Research Triangle Park, NC. North Carolina Supercomputing Center. ;National Oceanic and Atmospheric Administration, Research Triangle Park, NC. Atmospheric Sciences Modeling Div.;Environmental Protection Agency, Research Triangle Park, NC. National Exposure Research Lab.
Publisher 1995
Year Published 1995
Report Number EPA/600/A-95/121;
Stock Number PB96-116868
Additional Subjects Advection ; Atmospheric models ; Probability theory ; Sensitivity ; Air quality ; Statistical analysis ; Errors ; Numerical differentiation ; Computer programs ;
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB96-116868 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 02/29/1996
Collation 8p
Abstract
Air quality models simulate the fate of atmospheric pollutants using a set of algebraic and differential equations based upon the physical laws of science. Inevitably, model performance is influenced by errors and uncertainties introduced into the model by the parameterization schemes and the input data. Many sampling methods (e.g., the Monte Carlo method) have been widely used for model uncertainty calculations. When the model is complex, these methods require substantial computer resources and human effort for executing and managing model runs. Moreover, these methods provide only partial information unless every model run is executed with a complete set of input data. These disadvantages can be overcome with two techniques described in this paper: an automatic differentiation technique for calculating sensitivity and a statistical method for calculating the propagation of uncertainty in air quality models. These methods are demonstrated using a one-dimensional advection model.