Record Display for the EPA National Library Catalog


Main Title Resampling and Extreme Value Statistics in Air Quality Model Performance Evaluation.
Author Rao, S. T. ; Sistla, G. ; Pagnotti, V. ; Petersen, W. B. ; Irwin, J. S. ;
CORP Author Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Sciences Research Lab. ;New York State Dept. of Environmental Conservation, Albany.
Year Published 1985
Stock Number PB87-193629
Additional Subjects Air pollution ; Models ; Data processing ; Performance evaluation ; Value ; Hypotheses ; Statistical analysis ; Data sampling ; Reprints ; Air quality
Library Call Number Additional Info Location Last
NTIS  PB87-193629 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 18p
Specific data analysis techniques that will reveal the performance of air quality models in simulating the measured concentrations' cumulative distribution are discussed. The paper presents two types of analysis to compare model predictions with the measurements. In one analysis, extreme value statistics and the fitting of tail exponential distributions to both measured and predicted values are used in various ways to see if the measured and predicted values fit such distributions and to what degree the higher values of the cumulative frequency distributions coincide. In the second analysis, a resampling (bootstrap technique is used to develop non-parametric confidence intervals for the entire cumulative distribution of the measured concentrations, and to derive empirical distributions for central tendency statistics and for differences between measured and predicted mean and median values. The analysis is focused so as to show (1) why the resampling is necessary and the degree to which mistaken judgments can be made with and without the technique, and (2) comparisons between the discriminating capabilities of 'tail fit' type model evaluation and one using the resampling technique. It is shown that both the bootstrap and extreme value statistics are needed to quantify the uncertainty associated with the model predictions. (Copyright (c) Atmospheric Environment Vol. 19 No. 9, pp. 1503-1518, 1985.)