Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Objective Reduction of the Space-Time Domain Dimensionality for Evaluating Model Performance.
Author Gego, E. ; Porter, P. S. ; Hogrefe, C. ; Gilliam, R. ; Gilliland, A. ;
CORP Author Environmental Protection Agency, Research Triangle Park, NC. National Exposure Research Lab. ;State Univ. of New York at Albany.
Publisher 2004
Year Published 2004
Report Number EPA/600/A-04/116 ;NERL-RTP-AMD-04-096;
Stock Number PB2005-101227
Additional Subjects Performance evaluation ; Air quality ; Meteorological data ; Computerized simulation ; Pollution abatement ; Air pollution monitoring ; Performance ; Evaluation ; Air quality models ; Space-time domain
Library Call Number Additional Info Location Last
NTIS  PB2005-101227 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 05/04/2005
Collation 10p
In the United States, photochemical air quality models are the principal tools used by governmental agencies to develop emission reduction strategies aimed at achieving National Ambient Air Quality Standards (NAAQS). Before they can be applied with confidence in a regulatory setting, models' ability to simulate key features embedded in the air quality observations at an acceptable level must be assessed. With this concern in mind, the U.S. Environmental Protection Agency (EPA) has recently completed several executions of the Community Multiscale Air Quality model (CMAQ) and the Regional Modeling System for Aerosols and Deposition model (REMSAD) to simulate air quality over the contiguous United States during year 2001 with a horizontal cell size of 36 km x 36 km. The meteorological model MM5 and the emission processor SMOKE were used to generate the input fields necessary for CMAQ and REMSAD. Since these annual model simulation generate a huge amount of information, failure to properly organize the results may lead to confusion and hamper the evaluation procedure. The challenge is therefore to identify a technique that would make use of all pertinent observations over a large region and clearly indicate which spatial and temporal features are reproduced by the model. To address this challenge, we propose a procedure to objectively condense the spatial and temporal observational domain into a limited number of homogeneous categories.