Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Computational Modeling Issues in Next Generation Air Quality Models.
Author Byun, D. W. ; Hwang, D. ; Coats, C. J. ; Odman, M. T. ; Dennis, R. L. ;
CORP Author National Oceanic and Atmospheric Administration, Research Triangle Park, NC. Atmospheric Sciences Modeling Div. ;North Carolina Supercomputing Center, Research Triangle Park, NC. ;MCNC, Research Triangle Park, NC. Information Technologies Div.;Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Research and Exposure Assessment Lab.
Publisher 1994
Year Published 1994
Report Number EPA/600/A-94/148;
Stock Number PB94-197449
Additional Subjects Air quality ; Mathematical models ; Computerized simulation ; Computation ; US EPA ; Photochemistry ; Algorithms ; Performance evaluation ; Decision making ; Urban areas ; Regional analysis ; Prototypes ; Sensitivity ; Computer architecture ; Atmospheric models ; Environmental policy
Library Call Number Additional Info Location Last
NTIS  PB94-197449 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 11/11/1994
Collation 5p
The U.S. Environmental Protection Agency's Atmospheric Research and Exposure Assessment Laboratory is leading a major effort to advance urban/regional multi-pollutant air quality modeling through development of a third-generation modeling system, Models-3. The Models-3 system is being developed within a high-performance computing technology framework to take advantage of developments in science and the computer and communication fields. In the paper, the authors discuss several issues with Models-3 prototype examples. One area of important work is assessing the trade-off between speed and accuracy, either through development of new algorithms or optimizing existing ones to the computer architecture. The authors provide examples of such work on characterization of numerical algorithms. A second issue is how best to optimize codes for high performance architectures. Finally, the authors compare sensitivity estimation methods such as an automatic differentiation of the air quality model and a simple brute-force sensitivity analysis.