International Journal of Biometeorology

, Volume 52, Issue 8, pp 805–814 | Cite as

Numerical simulation of birch pollen dispersion with an operational weather forecast system

Original Paper


We included a parameterisation of the emissions of pollen grains into the comprehensive model system COSMO-ART. In addition, a detailed density distribution of birch trees within Switzerland was derived. Based on these new developments, we carried out numerical simulations of the dispersion of pollen grains for an episode that occurred in April 2006 over Switzerland and the adjacent regions. Since COSMO-ART is based on the operational forecast model of the German Weather Service, we are presenting a feasibility study of daily pollen forecast based on methods which have been developed during the last two decades for the treatment of anthropogenic aerosol. A comparison of the model results and very detailed pollen counts documents the current possibilities and the shortcomings of the method and gives hints for necessary improvements.


Pollen dispersion Numerical modelling Operational pollen forecast 


  1. Arealstatistik (1992/97) BFS GEOSTATGoogle Scholar
  2. Aylor DE (2002) Settling speed of corn (Zea mays) pollen. J Aerosol Sci 33:1601–1607, doi:10.1016/S0021–8502(02)00105–2 CrossRefGoogle Scholar
  3. Clot B (2001) Airborne birch pollen in Neuchâtel (Switzerland): onset, peak and daily patterns. Aerobiologia 17:25–29, doi:10.1023/A:1007652220568 CrossRefGoogle Scholar
  4. Comtois P (1998) Statistical analysis of aerobiological data. In: Mandrioli P, Comtois P and Levizzani V (eds) Methods in aerobiology. Pitagora, Bologna, pp 217–252Google Scholar
  5. Comtois P, Alcazar P, Néron D (1999) Pollen counts statistics and its relevance to precision. Aerobiologia 15:19–28, doi:10.1023/A:1007501017470 CrossRefGoogle Scholar
  6. Doms G, Schättler U (2002) A description of the Nonhydrostatic Regional Model LM, Deutscher Wetterdienst. PO Box 100465, 63004 Offenbach, Germany. (
  7. Friedlander SK (1977) Smoke, dust and haze. Wiley, New YorkGoogle Scholar
  8. Fritz A, Gressel W (1983) Zur Wetter-, insbesondere zur Temperaturabhängigkeit des Pollenfluges der Hasel, Birke und Gräser in Kärnten. med-met. Z Med Meteorologie 2:14–17Google Scholar
  9. Fuchs NA (1964) The mechanics of aerosols. Pergamon, OxfordGoogle Scholar
  10. Helbig N, Vogel B, Vogel H, Fiedler F (2004) Numerical modelling of pollen dispersion on the regional scale. Aerobiologia 20:3–19, doi:10.1023/B:AERO.0000022984.51588.30 CrossRefGoogle Scholar
  11. Jarosz N, Loubet B, Huber L (2004) Modelling airborne concentration and deposition rate of maize pollen. Atmos Environ 38:5555–5566, doi:10.1016/j.atmosenv.2004.06.027 CrossRefGoogle Scholar
  12. Menzel A, Dose V (2005) Analysis of long-term time series of the beginning of flowering by Bayesian function estimation. Meteorol Z 14:429–434, doi:10.1127/0941–2948/2005/0040 CrossRefGoogle Scholar
  13. Mullins J, Emberlin J (1997) Sampling pollens. J Aerosol Sci 28(3):365–370, doi:10.1016/S0021–8502(96)00439–9 CrossRefGoogle Scholar
  14. Pasken R, Pietrowicz JA (2005) Using dispersion and mesoscale meteorological models to forecast pollen concentrations. Atmos Environ 39:7689–7701, doi:10.1016/j.atmosenv.2005.04.043 CrossRefGoogle Scholar
  15. Puls KE (1987) Der Einfluß von Witterung und Wetter auf Blütenanlage, Pollenfreisetzung und Pollenflug. In: Stiftung Deutscher Polleninformationsdienst (eds) 1. Europäisches Pollenflug-Symposium 20/21 März: 27–47Google Scholar
  16. Rempe H (1938) Untersuchungen über die Verbreitung des Blütenstaubes durch die Luftströmungen. Planta 27:93–147, doi:10.1007/BF01939376 CrossRefGoogle Scholar
  17. Riemer N, Vogel H, Vogel B, Fiedler F (2003) Modelling aerosols on the mesoscale-g: Treatment of soot aerosol and its radiative effects. J Geophys Res 109:4601, doi:10.1029/2003JD003448 CrossRefGoogle Scholar
  18. Schüler S, Schluenzen KH (2006) Modeling of oak pollen dispersal on the landscape level with a mesoscale atmospheric model. Environ Model Assess 11:1420–2026, doi:10.1007/s10666–006–9044–8 Google Scholar
  19. Siljamo P, Sofiev M, Ranta H (2004) An approach to simulation of long-range atmospheric transport of natural allergens: an example of birch pollen. In Air Pollution Modelling and its Applications XVII (in press.), also in pre-prints of 27-th Int. Technical Meeting on Air Pollution Modelling and its Applications, Banff, 23–30.10.2004, Canada, pp 395–402Google Scholar
  20. Slinn WGN (1983) Air-to-sea transfer of particles. In: Liss PS, Slinn WGN (eds) Air-sea exchange of gases and particles. Reidel, Dortrecht, pp 299–405Google Scholar
  21. Sofiev M, Siljamo P (2003) Forward and inverse simulations with Finnish emergency model SILAM. In: Borrego C, Incecik S (eds) Air pollution modelling and its applications XVI. Kluwer, Dordrecht, pp 417–425Google Scholar
  22. Sofiev M, Siljamo P, Ranta H, Rantio-Lehtimäki A (2006) Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study. Int J Biometeorol 50:392–402, doi:10.1007/s00484–006–0027-x PubMedCrossRefGoogle Scholar
  23. Vogel B, Hoose C, Vogel H, Kottmeier C (2006) A model of dust transport applied to the Dead Sea Area. Meteorol Z 15:611–624, doi:10.1127/0941–2948/2006/0168 CrossRefGoogle Scholar
  24. Wachter R (1982) Pollen- und Sporenflug über der Bundesrepublik Deutschland. 14. Allergopharma. Joachim Ganzer KGGoogle Scholar
  25. Walklate PJ, Hunt JCR, Higson HL, Sweet JB (2004) A model of pollen-mediated gene flow for oilseed rape. Proc Biol Sci 271:441–449, doi:10.1098/rspb.2003.2578 PubMedCrossRefGoogle Scholar
  26. WHO (2003) Phenology and human health: allergic disorders. Copenhagen, WHO Regional Office for Europe, p 55Google Scholar
  27. WSL (2006) Schweizerisches Landesforstinventar LFI. Datenbankauszug der Erhebung 1983–85 vom 27. Juni 2006. Ulrich Ulmer. Eidg. Forschungsanstalt WSL, BirmensdorfGoogle Scholar

Copyright information

© ISB 2008

Authors and Affiliations

  1. 1.Institut für Meteorologie und KlimaforschungForschungszentrum Karlsruhe/Universität KarlsruheKarlsruheGermany
  2. 2.Bundesamt für Meteorologie und Klimatologie MeteoSchweizZürichSwitzerland

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