Record Display for the EPA National Library Catalog

RECORD NUMBER: 4 OF 11

Main Title Convective Diffusion Field Measurements Compared with Laboratory and Numerical Experiments.
Author Briggs, G. A. ; Eberhard, W. L. ; Gaynor, J. E. ; Moninger, W. R. ; Uttal, T. ;
CORP Author Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Sciences Research Lab. ;National Oceanic and Atmospheric Administration, Boulder, CO. Wave Propagation Lab.
Year Published 1986
Report Number EPA/600/D-86/236;
Stock Number PB87-104634
Additional Subjects Atmospheric diffusion ; Atmospheric chemistry ; Numerical analysis ; Convective flow ; Convection(Atmospheric) ; Gaussian plume models ; CONDORS experiment
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB87-104634 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 7p
Abstract
Some of the more fundamental diffusion parameters measured in the CONDORS convective diffusion field experiment are compared with laboratory experiment and numerical modeling results by means of nondimensionalizations using convective scaling (i.e., mixing depth, z sub i, for length and w* for velocity). The CONDORS experiment used remote sensors, radar and lidar, to measure three-dimensional patterns of metalicized 'chaff' and oil fog. The growth of the vertical standard deviation of plume distribution, sigma sub z, agrees quite well with the non-field results, approximating 0.6 w*t nearly to the point of limitation by capping at z = z sub i. The lateral standard deviation, sigma sub y, also tends to approximate 0.6 w*t for most elevated releases, while most surface releases show slower growth at large t that better approximates the non-field results. Surface patterns of crosswind-integrated concentration show remarkable agreement with the laboratory results, for the most part, although there is more variation in individual runs; peak values from elevated releases are on the order of 70% larger than conventional Gaussian model predictions.