Record Display for the EPA National Library Catalog

RECORD NUMBER: 15 OF 21

OLS Field Name OLS Field Data
Main Title Stochastic Methodology for Regional Wind-Field Modeling.
Author Schere, K. L. ; Coats, C. J. ;
CORP Author Computer Sciences Corp., Research Triangle Park, NC. ;National Oceanic and Atmospheric Administration, Research Triangle Park, NC. Atmospheric Sciences Modeling Div.;Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Research and Exposure Assessment Lab.
Publisher c1992
Year Published 1992
Report Number EPA-68-01-7365; EPA/600/J-93/318;
Stock Number PB93-229391
Additional Subjects Stochastic processes ; Wind(Meteorology) ; Air quality ; Computerized simulation ; Mesoscale phenomena ; Three-dimensional calculations ; Regional analysis ; Air pollution ; Error analysis ; Environmental transport ; Meteorology ; Interpolation ; Reprints ; Regional Oxidant Model ; Northeast Region(United States)
Holdings
Library Call Number Additional Info Location Last
Modified
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Status
NTIS  PB93-229391 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 11/22/1993
Collation 21p
Abstract
Three-dimensional regional scale (approximately 1000 km) air quality simulation models require hourly inputs of U and V wind components for each vertical layer of the model and for each grid cell in the horizontal. The standard North American meteorological observation network is used to derive the wind field inputs for the U.S. EPA's Regional Oxidant Model (ROM) and other regional models. While a fairly dense surface network with hourly observations exists, upper-air data are obtained only twice per day at monitoring sites typically separated by distances of 300-500 km. Using these data to derive the more spatially and temporally resolved gridded wind fields needed by the ROM introduces uncertainties and errors into the model. The authors present a method of developing gridded wind fields for the ROM that accounts for these non-deterministic features. The method produces a family of potential gridded wind fields that allows for the stochastic nature of the interpolation process. Examples of the derived wind fields are given for the Northeast United States. Potential differences between wind fields, in terms of their effects on air quality modeling, are inferred from following multi-day flow trajectories using various members of the wind field family. The sensitivity of results to the density of surface observational data is also presented.