Hostname: page-component-5c6d5d7d68-lvtdw Total loading time: 0 Render date: 2024-08-15T18:00:56.740Z Has data issue: false hasContentIssue false

RODOS meteorological pre-processor and atmospheric dispersion model DIPCOT: a model suite for radionuclides dispersion in complex terrain

Published online by Cambridge University Press:  16 September 2010

S. Andronopoulos
Affiliation:
NCSR Demokritos, Institute of Nuclear Technology and Radiation Protection, Environmental Research Laboratory, 15310 Aghia Paraskevi, Greece
E. Davakis
Affiliation:
NCSR Demokritos, Institute of Nuclear Technology and Radiation Protection, Environmental Research Laboratory, 15310 Aghia Paraskevi, Greece
J.G. Bartzis
Affiliation:
Department of Mechanical Engineering, University of Western Macedonia, Bakola & Sialvera, 50100 Kozani, Greece
I. Kovalets
Affiliation:
Institute of Mathematical Machines and Systems Problems, NAS of Ukraine, Ukraine
Get access

Abstract

The Meteorological Pre-Processor (MPP) of the Decision Support System RODOS acts as interface between the incoming meteorological data from stations and/or prognostic models and the Atmospheric Dispersion Models (ADMs) used for predicting the spread of the accidentally emitted radionuclides. The MPP includes a diagnostic Wind Field Model (WFM) to ensure mass conservation of the calculated wind field. Its output is usable by simple and complex ADMs and it is applicable for highly complex topography and from micro- to meso-scales. The MPP has been tested for both real and artificial flow fields and it has been optimized to function with very short execution times and to give the most reasonable results under all terrain complexity and atmospheric stability conditions. DIPCOT (DIsPersion over COmplex Terrain) is a Lagrangian Puff / Particle model that has been implemented in RODOS to simulate radionuclides atmospheric dispersion over complicated terrain. For this purpose, it uses a certain number of fictitious puffs/particles which are assumed to move with the mean wind flow plus a random velocity component to simulate turbulent diffusion. The calculation of the gamma radiation dose rates in air due to the radioactive plume is calculated by a very fast method that takes into account the inhomogeneous 3-dimensional cloud shape. DIPCOT has been evaluated by comparisons to widely used real-scale experimental data sets: Copenhagen, Prairie Grass, Indianapolis and Mol. The integration of the above models greatly enhances the applicability of the RODOS system.

Type
Article
Copyright
© EDP Sciences, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andronopoulos S., Bartzis J.G. (2009a) Model description of the RODOS Meteorological Pre-Processor, Report RODOS(RA2)-TN(09)02, available in http://www.rodos.fzk.de/rodos.html.
Andronopoulos S., Bartzis J.G. (2009b) Report on performance tests of the mass-consistent wind-field model, Report EURANOS(CAT2)-TN(09)04.
Andronopoulos S., Davakis E., Bartzis J.G. (2009) RODOS-DIPCOT Model Description and Evaluation, Report RODOS(RA2)-TN(09)01, available in http://www.rodos.fzk.de/rodos.html.
Gryning, S.E., Holstag, A.A.M., Irwin, J.S., Sivertsen, B. (1987) Applied dispersion modelling based on meteorological scaling parameters, Atmos. Environ. 21, 7989.CrossRefGoogle Scholar
Hanna S.R. (1982) Atmospheric turbulence and air pollution modelling. In: Atmospheric Turbulence and Air Pollution Modelling (Nieuwstadt F.M.T., van Dop H., Ed.) pp. 275-310, D. Reidel, Dordrecht, Holland.
Hanna, S.R., Chang, J.C. (1993) Hybrid plume dispersion model (HPDM) improvements and testing at three field sites, Atmos. Environ. 27A, 1591-1508.Google Scholar
Holtslag, A.A.M., Moeng, C.H. (1991) Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer, J. Atmos. Sci. 48, 1690-1998.2.0.CO;2>CrossRefGoogle Scholar
IAEA (1980) Atmospheric Dispersion in Nuclear Power Plant Siting. International Atomic Energy Agency, Safety Series, 50-SG-S3.
Kitada, T., Kaki, A., Ueda, H., Peters, L.K. (1983) Estimation of vertical air motion from limited horizontal wind data-a numerical experiment, Atmos. Environ. 17, 2181-2192.CrossRefGoogle Scholar
Kovalets I. (2006) RODOS System Meteorological and Atmospheric Dispersion Module functionality enhancement by introduction of numerically efficient algorithms. Final Report of the “RODOS / METADM – enhance” Project Contract Nº 516492 (FI6R).
Ratto, C.F., Festa, R., Romeo, C., Frumento, O.A., Galluzzi, M. (1994) Mass-consistent models for wind fields over complex terrain: The state of the art, Environ. Softw. 9, 247-268.CrossRefGoogle Scholar
Sasaki, Y. (1958) An objective analysis based on the variational method, J. Met. Soc. Japan 36, 77-88.CrossRefGoogle Scholar
Sasaki, Y. (1970) Some basic formalism in numerical variational analysis, Mont. Weather Rev. 98, 875-883.2.3.CO;2>CrossRefGoogle Scholar
Seibert P., Beyrich F., Gryning S.-E., Joffre S., Rasmussen A., Tercier Ph. (1997) Mixing Height Determination for Dispersion Modelling, Report of Working Group 2. In: COST Action 710 – Final Report, EUR 18195 EN. European Commission.
Taylor, G.I. (1921) Diffusion by continuous movements, Proc. Lond. Math. Soc. 20, 169-211.Google Scholar
Thomson, D.J. (1987) Criteria for the selection of stochastic models of particle trajectories in turbulent flows, J. Fluid Mech. 180, 529-556.CrossRefGoogle Scholar