• Briggs, G. A. 1993. Final results of the CONDORS convective diffusion experiment. Bound.-Layer Meteor 62:315328.

  • Fox, D. 1984. Uncertainty in air quality modeling. Bull. Amer. Meteor. Soc 65:2736.

  • Lamb, R. G. 1982. Diffusion in the convective boundary layer. Atmospheric Turbulence and Air Pollution Modeling, F. T. M. Nieuwstadt and H. van Dop, Eds., D. Reidel, 159–229.

    • Search Google Scholar
    • Export Citation
  • Snyder, W. H. 1981. Guideline for fluid modeling of atmospheric diffusion. U.S. Environmental Protection Agency Rep. EPA-600/8-81-009, 185 pp.

    • Search Google Scholar
    • Export Citation
  • Tennekes, H. and J. L. Lumley. 1972. A First Course in Turbulence. MIT Press, 300 pp.

  • Thompson, R. S., W. H. Snyder, and J. C. Weil. 2000. Laboratory simulation of the rise of buoyant thermals created by open detonation. J. Fluid Mech 417:127156.

    • Search Google Scholar
    • Export Citation
  • Townsend, A. A. 1956. The Structure of Turbulent Shear Flow. Cambridge University Press, 315 pp.

  • Venkatram, A. 1979. The expected deviation of observed concentrations from a predicted ensemble mean. Atmos. Environ 13:15471549.

  • Venkatram, A. 1984. The uncertainty in estimating dispersion in the convective boundary layer. Atmos. Environ 18:307310.

  • Venkatram, A. 1988. Inherent uncertainty in air quality modeling. Atmos. Environ 22:12211227.

  • Venkatram, A. and J. C. Wyngaard. 1988. Lectures on Air Pollution Modeling. Amer. Meteor. Soc., 390 pp.

  • Weil, J. C., R. I. Sykes, and A. Venkatram. 1992. Evaluating air-quality models: Review and outlook. J. Appl. Meteor 31:11211145.

  • Willis, G. E. and J. W. Deardorff. 1976. A laboratory model of diffusion into the convective planetary boundary layer. Quart. J. Roy. Meteor. Soc 102:427445.

    • Search Google Scholar
    • Export Citation
  • Willis, G. E. and J. W. Deardorff. 1978. A laboratory study of dispersion from an elevated source within a modeled convective planetary boundary layer. Atmos. Environ 12:13051311.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 203 41 16
PDF Downloads 33 9 2

Fluid Modeling and the Evaluation of Inherent Uncertainty

Ariel F. SteinDepartment of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Ariel F. Stein in
Current site
Google Scholar
PubMed
Close
and
John C. WyngaardDepartment of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by John C. Wyngaard in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Atmospheric dispersion models are widely used to estimate the impact of nonreactive pollutant releases into the atmosphere. Most such models predict the ensemble-average concentration field, the average over a large collection of releases under similar externally imposed conditions. The stochastic nature of pollutant dispersion in the atmospheric boundary layer (ABL) makes pollutant concentrations vary greatly from one sampling period to another under the same meteorological conditions, however. Therefore, if the sampling time is not sufficiently long, the values predicted by even a perfect model will inevitably differ from mean values measured in the atmosphere. The ratio of their root-mean-square difference and the ensemble-average concentration is called the inherent uncertainty. This work investigates the relationship between inherent uncertainty in laboratory and ABL flows. This relationship provides a way to use laboratory measurements to estimate the inherent uncertainty in ABL flows. For a given averaging time, it is shown that the inherent uncertainty in laboratory flows is smaller than in the ABL under the same stability and statistical conditions. This result is illustrated with the Willis–Deardorff convection tank data to show that the values of the inherent uncertainty in pollutant dispersion from a near-surface source in the ABL exceed 50% for an averaging time on the order of 1 h.

Corresponding author address: Ariel F. Stein, Dept. of Meteorology, The Pennsylvania State University, 503 Walker Bldg., University Park, PA 16802. ais5@psu.edu

Abstract

Atmospheric dispersion models are widely used to estimate the impact of nonreactive pollutant releases into the atmosphere. Most such models predict the ensemble-average concentration field, the average over a large collection of releases under similar externally imposed conditions. The stochastic nature of pollutant dispersion in the atmospheric boundary layer (ABL) makes pollutant concentrations vary greatly from one sampling period to another under the same meteorological conditions, however. Therefore, if the sampling time is not sufficiently long, the values predicted by even a perfect model will inevitably differ from mean values measured in the atmosphere. The ratio of their root-mean-square difference and the ensemble-average concentration is called the inherent uncertainty. This work investigates the relationship between inherent uncertainty in laboratory and ABL flows. This relationship provides a way to use laboratory measurements to estimate the inherent uncertainty in ABL flows. For a given averaging time, it is shown that the inherent uncertainty in laboratory flows is smaller than in the ABL under the same stability and statistical conditions. This result is illustrated with the Willis–Deardorff convection tank data to show that the values of the inherent uncertainty in pollutant dispersion from a near-surface source in the ABL exceed 50% for an averaging time on the order of 1 h.

Corresponding author address: Ariel F. Stein, Dept. of Meteorology, The Pennsylvania State University, 503 Walker Bldg., University Park, PA 16802. ais5@psu.edu

Save