||Aggregation and Episode Selection Scheme for EPA's Models-3 CMAQ.
Eder, B. K. ;
Cohn, R. D. ;
LeDuc, S. K. ;
Dennis, R. L. ;
||Environmental Protection Agency, Research Triangle Park, NC. National Exposure Research Lab. ;Analytical Sciences, Inc., Durham, NC. ;National Oceanic and Atmospheric Administration, Research Triangle Park, NC. Air Resources Lab.
Air pollution monitoring ;
Cluster analysis ;
Air quality data ;
Atmospheric composition ;
Ecological concentration ;
Seasonal variations ;
Meteorological data ;
Community Multiscale Air Quality(CMAQ) ;
Models-e modeling system
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||The development of an episode selection and aggregation approach, designed to support distributional estimation for use with the Models-3 Community Multiscale Air Quality (CMAQ) model, is described. The approach utilized cluster analysis of the 700 hPa u and v wind field components over the time period 1984-92 to define homogeneous meterological clusters. Alternative schemes were compared using relative efficiencies and meteorological considerations. An optimal scheme was defined to include 20 clusters (five per season), and a stratified sample of 40 events was selected from the 20 clusters using a systematic sampling technique. The light-extinction coefficient, which provides a measure of visibility, was selected as the primary evaluative parameter for two reasons. First, this parameter can serve as a surrogate for PM-2.5, for which little observational data exist. Second, one of the air quality parameters simulated by CMAQ, this visibility parameter has one of the most spatially and temporally comprehensive observational data sets. Results suggest that the approach reasonably characterizes synoptic-scale flow patterns and leads to strata that explain the variation in extinction coefficient and other parameters (temperature and relative humidity) used in this analysis, and therefore can be used to achieve improved estimates of these parameters relative to estimates obtained using other methods. Moreover, defining seasonally based clusters further improves the ability of the clusters to explain the variation in these parameters.
||Prepared in cooperation with Analytical Sciences, Inc., Durham, NC. and National Oceanic and Atmospheric Administration, Research Triangle Park, NC. Air Resources Lab.
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||PC A02/MF A01