On-line Integrated Meteorological and Chemical Transport Modelling: Advantages and Prospectives

  • Alexander Baklanov
  • Ulrik Korsholm
Part of the NATO Science for Peace and Security Series Series C: Environmental Security book series (NAPSC)


The strategy for developing new-generation integrated Meso-Meteorological (MetM) and Atmospheric Chemical Transport Model (CTM) systems is discussed and an overview of the European COST728 ( integrated systems is given. Advantages and disadvantages of on-line integration versus the more common off-line coupling of MetMs and CTMs are mentioned using DMI-ENVIRO-HIRLAM (HIgh Resolution Limited Area Model) as a specific example. Current progress in the DMI-ENVIRO-HIRLAM system development and its urban on-line coupled modelling applications are considered. Several sensitivity tests of off-line versus on-line coupling in DMI-ENVIRO-HIRLAM as well as verification versus the ETEX experiment are considered, and results are discussed.


Aerosol feedbacks chemical weather forecast climate change ENVIRO-HIRLAM system integrated models on-line coupling 


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  1. AIRES (2001) AIRES in ERA, European Commission, EUR 19436.Google Scholar
  2. Baklanov A (1988) Numerical modelling in mine aerology, Apatity: USSR Aca-demy of Science, 200 p. (in Russian).Google Scholar
  3. Baklanov A (2005) Meteorological advances and systems for urban air quality forecasting and assessments. Short Papers of the 5th International Conference on Urban Air Quality Valencia, Spain, 29-31 March 2005, CLEAR, pp. 22-25.Google Scholar
  4. Baklanov A, Gross A, Sørensen JH (2004) Modelling and forecasting of regional and urban air quality and microclimate. J. Comput. Technol., 9:82-97.Google Scholar
  5. Baklanov A, Fay B, Kaminski J, Sokhi R (2007a) Overview of existing integrated (off-line and on-line) mesoscale meteorological and chemical transport modelling systems in Europe. Joint Report of COST Action 728 and GURME, May 2007. WMO TD No. 1427, GAW Report No. 177. Available from http://www.cost728. org
  6. Baklanov A, Hänninen O, Slørdal LH, Kukkonen J, Bjergene N, Fay B, Finardi S, Hoe SC, Jantunen M, Karppinen A, Rasmussen A, Skouloudis A, Sokhi RS, Sørensen JH, Ødegaard V (2007b) Integrated systems for forecasting urban meteorology, air pollution and population exposure. Atmos. Chem. Phys., 7:855-874.Google Scholar
  7. Baklanov A, Korsholm U, Mahura A, Petersen C, Lindberg K, Gross A, Rasmussen A, Sørensen JH, Amstrup B, Chenevez J (2008) ENVIRO-HIRLAM on-line coupled modelling of urban meteorology and air pollution. Adv. Sci. Res., 2, 41-46.CrossRefGoogle Scholar
  8. Chenevez J, Baklanov A, Sørensen JH (2004) Pollutant transport schemes integ-rated in a numerical weather prediction model: model description and verifi-cation results. Meteorol. Appl., 11(3):265-275.CrossRefGoogle Scholar
  9. COSMOS: Community Earth System Models Integrating strategy. Web-site:
  10. Dickenson RE, Zebiak SE, Anderson JL, Blackmon ML, DeLuca C, Hogan TF, Iredell M, Ji M, Rood R, Suarez MJ, Taylor KE (2002) How can we advance our weather and climate models as a community? Bull. Am. Met. Soc., 83:431-434. EMS-FUMAPEX (2005) “Urban Meteorology and Atmospheric Pollution”,Google Scholar
  11. Baklanov A, Joffre S, Galmarini S (Eds.). Special Issue of Atmospheric Chemistry and Physics Journal. Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B (2005) Fully coupled “online” chemistry within the WRF model. Atmos. Environ., 39(37):6957-6975.Google Scholar
  12. Gross A, Baklanov A (2004) Modelling the influence of dimethyl sulphide on the aerosol production in the marine boundary layer. Int. J. Environ. Pollut., 22 (1/2):51-71.Google Scholar
  13. IPCC (2001) Climate Change 2001, The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), edited by L. Houghton et al., Cambridge University Press, Cambridge, United Kingdom/New York.Google Scholar
  14. IPCC (2005) IPCC Expert Meeting on Emission Estimation of Aerosols Relevant to Climate Change held on 2-4 May 2005, Geneva, Switzerland. Korsholm U, Baklanov A, Mahura A, Petersen C, Lindberg K, Gross A, Rasmussen A, Sørensen JH, Chenevez J (2006) ENVIRO-HIRLAM. An On-Line Coupled Multi-Purpose Environment Model. ACCENT/GLOREAM Workshop 2006 Proceedings.
  15. Korsholm U, Baklanov A, Gross A, Sørensen JH (2007) On the importance of the meteorological coupling interval in air pollution modeling, submitted to Atm. Env.: Special Issue COST728, UAQ2007.Google Scholar
  16. Jacobson MZ (2002) Atmospheric Pollution: History, Science and Regulation. Cambridge University Press, New York.Google Scholar
  17. Jacobson MZ (2005) Fundamentals of Atmospheric Modeling, Second Edition. Cambridge University Press, New York, 813 pp. Google Scholar
  18. Jacobson MZ (2006) Comment on “Fully coupled ‘online’ chemistry within the WRF model, ” by Grell et al., Atmos. Environ., 39:6957-6975.Google Scholar
  19. Marchuk GI (1982) Mathematical modeling in the environmental problems. Nauka, Moscow.Google Scholar
  20. Penenko VV, Aloyan AE (1985) Models and methods for environment protection problemsNovosibirsk, Nauka (in Russian).Google Scholar
  21. Penner JE et al. (1998) Climate forcing by carbonaceous and sulphate aerosols. Clim. Dynam., 14:839-851.CrossRefGoogle Scholar
  22. Uno I et al. (2004) Numerical study of Asian dust transport during the springtime of 2001 simulated with the Chemical Weather Forecasting System (CFORS) model. J. Geophys. Res., 109, D19S24, doi:10.1029/2003JD004222.CrossRefGoogle Scholar
  23. Uno I et al., (2003) Regional chemical weather forecasting system CFORS: model descriptions and analysis of surface observations at Japanese island stations during the ACE-Asia experiment, J. Geophys. Res., 108 (D23), 8668, doi: 10.1029/2002JD002845.CrossRefGoogle Scholar
  24. Semazzi F (2003) Air quality research: perspective from climate change modelling research. Environment International, 29:253-261.CrossRefGoogle Scholar
  25. Shine KP (2000) Radiative forcing of climate change. Space Sci. Rev. 94:363-373. Valcke S, Guilyardi E, Larsson C (2006) PRISM and ENES: a European approach to Earth system modelling. Concurrency. Comput. Pract. Exp. 18:231-245.Google Scholar
  26. Vogel B, Hoose C, Vogel H, Kottmeier Ch (2006) A model of dust transport applied to the Dead Sea area. Meteorologische Zeitschrift, 14:611-624.CrossRefGoogle Scholar
  27. Watson RT et al. (1997) The regional impacts of climate change: an assessment of vulnerability. Special Report for the Intergovernmental Panel on Climate Change.Google Scholar
  28. Wolke R, Hellmuth O, Knoth O, Schröder W, Heinrich B, Renner E (2003) The chemistry-transport modeling system LM-MUSCAT: description and CITYDELTA applications. Proceedings of the 26th International Technical Meeting on Air Pollution and Its Application. Istanbul, May 2003, 369-379.Google Scholar
  29. Yeh K-S, Cote J, Gravel S, Methot A, Patoine A, Roch M, Staniforth A (2002) The CMC-MRB global environmental multiscale (GEM) model. Part III: Nonhydrostatic formulation. Mon. Wea. Rev., 130, 2, 339-356.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V 2008

Authors and Affiliations

  • Alexander Baklanov
    • 1
  • Ulrik Korsholm
  1. 1.Meteorological ResearchDanish Meteorological Institute (DMI)CopenhagenDenmark

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