Regulatory-Oriented Features of the Kinematic Simulation Particle Model

  • Arno Graff
  • David Strimaitis
  • Robert Yamartino
Part of the NATO • Challenges of Modern Society book series (NATS, volume 22)


Realizing the need for a dispersion model which explicitly considers non-steady state emissions and arbitrarily complex, space-time varying meteorological conditions, and the desirability of having a model which can yield the probability distribution function (PDF) of concentrations rather than simply the ensemble mean, the German Federal Environmental Agency (UBA) funded the development and preliminary testing of an advanced atmospheric dispersion model, referred to as the Kinematic-Simulation-Particle (KSP) Model. The KSP modeling system (Yamartino et al.; 1993–6, and Strimaitis et al., 1995) includes dry and wet removal processes, many other of the processes (e. g., stack and building downwash, plume rise, partial lid penetration) included in the CALPUFF (Scire et al.; 1990, 1995) puff model, and computes the space-time average and percentile concentrations required by German and EC legislation; however, KSP also has the intrinsic capacity to predict the short-term concentration fluctuations that are critical to the assessment of odor and hazardous chemical exposure problems. A key model input involves the 3-d gridded mesoscale wind fields and accompanying 2-d fields specifying boundary layer quantities. These data may be provided by an external wind field/PBL model, or, in the case where only single-point measures of wind and stability are available, an internal flow and turbulence generator creates a vertical profile which emulates the dispersion conditions specified in the German regulatory guideline (TA-Luft). The current KSP model also accesses data produced by the CALMET diagnostic model (Scire et al., 1990; and U.S. EPA, 1995), and an interface to the GESIMA prognostic model has been developed by Reimer et al. (1994) that allows the modeled TKE budgets to be used in the computation of turbulence fields. Expansion of this interface to permit METRAS (Schluenzen et al., 1994) generated winds and turbulence to be utilized is also envisioned.


Tracer Experiment Atmospheric Dispersion Plume Rise Kinematic Simulation Gaussian Plume Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. AUSTAL-86, 1987: Richtlinie zur Durchführung von Ausbreitungs-rechnungen nach TA Luft mit dem Programmsystem AUSTAL86, November 1986, ©1987 Bundesanzeiger Verlagsges m. b. H., Köln, Germany.Google Scholar
  2. Barad, M.L., (Ed.), 1958: Project Prairie Grass, A field program in diffusion, Vol. 1, Geophysical Research Papers, No. 59, AFCRC-TR-58-235(I), Air Force Cambridge Research Center, Bedford, MA.Google Scholar
  3. Briggs, G.A., 1982: Similarity forms for ground source surface layer diffusion, Bound. Lay. Meteorol., 23, 489–502.CrossRefGoogle Scholar
  4. Eppel, D., H. Kapitza, M. Claussen, D. Jacob, L. Levkov, H.T. Mengelkamp, and N. Werrmann, 1994: The non-hydrostatic mesoscale model GESIMA. Part II: parameterizations and application. To be published in Beitr. Phys. Atmosph. Google Scholar
  5. Fung, J.C.H., J.C.R. Hunt, N.A. Malik and R.J. Perkins, 1992: Kinematic simulation of homogeneous turbulent flows generated by unsteady random Fourier modes. J. Fluid Mech., 236, 281–318.CrossRefGoogle Scholar
  6. Grønskei, K.E., 1990: Variation in dispersion conditions with height over urban areas-results of dual tracer experiments, 9th AMS Symposium on Turbulence and Diffusion.Google Scholar
  7. Gryning, S.E., 1981: Elevated source SF6 tracer dispersion experiments in the Copenhagen area. Riso-R-446, Riso National Laboratory, 187 pp.Google Scholar
  8. Gryning, S.E. and E. Lyck, 1984: Atmospheric dispersion from elevated sources in an urban area: Comparison between tracer experiments and model calculations. J. Cl. Appl. Meteor., 23, 651–660.CrossRefGoogle Scholar
  9. Hanna, S.R., J.C. Chang, and D.G. Strimaitis, 1990: Uncertainties in source emission rate estimates using dispersion models. Atmospheric Environment, 24A, 2971–2980.Google Scholar
  10. Haugsbakk, I. and D.A. Tønnesen, 1989: Atmospheric Dispersion Experiments at Lillestrøm, 1986–1987 Data Report, Lillestrom, Norwegian Institute for Air Research (NILU OR 31/89).Google Scholar
  11. Hojstrup, J., 1982: Velocity spectra in the unstable planetary boundary layer. J. Atmos. Sci., 39, 2239–2248.CrossRefGoogle Scholar
  12. Kapitza, H., and D. Eppel, 1992: The non-hydrostatic mesoscale model GESIMA. Part I: dynamic equations and tests. Beitr. Phys. Atmosph, 65, 129–146.Google Scholar
  13. Janicke, L., 1990: Ausbreitungsmodell LASAT-C: Handbuch Version 1.00. (In German)Google Scholar
  14. Janicke, L., 1995: Ausbreitungsmodell LASAT: Referenzbuch zu Version 2.51, Primelweg 8, D-88662, Überlingen, FRG. (In German)Google Scholar
  15. Moraes, O.L.L., 1988: The velocity spectra in the stable atmospheric boundary layer. Boundary Layer Meteorol., 43, 223–230.CrossRefGoogle Scholar
  16. Nieuwstadt, F.T.M., 1980: Application of mixed-layer similarity to the observed dispersion from a ground-level source, J. Appl. Meteorol., 19, 157–162.CrossRefGoogle Scholar
  17. Reimer, E., B. Scherer, W. Klug, and R. Yamartino, 1994. A Meteorological Data Base System for Next Generation Dispersion Models and a Lagrangian Particle Model Based on Kinematic Simulation Theory. Proceedings of the Workshop on Intercomparison of Atmospheric Dispersion Modeling Systems, Mol, Belgium.Google Scholar
  18. Schluenzen, K.H., K. Bigalke, U. Niemeier and K. von Salzen, 1994. The mesoscale transport-and fluid-model’ METRAS’-model concept, model realization-Meteorological Institute, University of Hamburg, METRAS Technical Report, 1, pp 150.Google Scholar
  19. Scire, J.S., E.M. Insley and R.J. Yamartino, 1990: Model formulation and user’s guide for the CALMET meteorological model. Prepared for the California Air Resources Board. Sigma Research Corporation, Concord, MA.Google Scholar
  20. Scire, J.S., D.G. Strimaitis and R.J. Yamartino, 1990: Model formulation and user’s guide for the CALPUFF dispersion model. California Air Resources Board, Sacramento, CA.Google Scholar
  21. Scire, J.S., D.G. Strimaitis, R.J. Yamartino and X. Zhang, 1995: A User’s Guide for the CALPUFF Dispersion Model. EARTH TECH Document 1321-2 prepared for the USD A Forest Service, Cadillac, MI.Google Scholar
  22. Strimaitis, D., R. Yamartino, E. Insley, and J. Scire, 1995. A User’s Guide for the Kinematic Simulation Particle Model. EARTH TECH Document 1274-2 prepared for the Free University of Berlin and the Umweltbundesamt, Berlin, FRG.Google Scholar
  23. U.S. EPA, 1995. A User’s Guide for the CALMET Meteorological Model. EPA-454/B-95-002.Google Scholar
  24. van Ulden, A.P., 1978: Simple estimates for vertical diffusion from sources near the ground, Atmospheric Environment, 12, 2125–2129.CrossRefGoogle Scholar
  25. Yamartino, R., D. Strimaitis, M. Spitzak, and W. Klug, 1993. Development and Preliminary Evaluation of a Dispersion Model Based on Kinematic Simulation Theory. Proceedings of the Workshop on Intercomparison of Atmospheric Dispersion Modeling Systems, Manno, Switzerland.Google Scholar
  26. Yamartino, R., D. Strimaitis, and A. Graff, 1995. Advanced Mesoscale Dispersion Modeling Using Kinematic Simulation. Proceedings of the 21st NATO/CCMS ITM on Air Polluting Modeling and its Application, Baltimore, MD, Nov. 6-10.Google Scholar
  27. Yamartino, R., D. Strimaitis, J. Scire, E. Insley, and M. Spitzak, 1996. Final Report on the Phase I Development of the Kinematic Simulation Particle (KSP) Atmospheric Dispersion Model. EARTH TECH Report 1274-3 prepared for the Free University of Berlin and the Umweltbundesamt, Berlin, FRG.Google Scholar
  28. Yamartino, R., D. Strimaitis, and A. Graff, 1996. Evaluation of the Kinematic Simulation Particle Model Using Tracer Experiments. Proceedings of the Fourth Workshop on Harmonization within Atmospheric Dispersion Modelling for Regulatory Purposes, Oostende, Belgium, May 6–9.Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Arno Graff
    • 1
  • David Strimaitis
    • 2
  • Robert Yamartino
    • 2
  1. UmweltbundesamtBerlinGermany
  2. 2.EARTH TECH, Inc.ConcordUSA

Personalised recommendations