Boundary-Layer Meteorology

, Volume 137, Issue 3, pp 373–396 | Cite as

Simulation of Pollutant Transport in Complex Terrain with a Numerical Weather Prediction–Particle Dispersion Model Combination

Article

Abstract

A new scaling approach, based on the convective velocity obtained from the sun-exposed eastern slopes and thus suited for steep and narrow Alpine valleys, is investigated with respect to pollutant dispersion. The capability of the new method is demonstrated with the operational emergency response system of MeteoSwiss, which consists of the COSMO (COnsortium for Small-scale MOdelling) numerical weather prediction model coupled with a Lagrangian particle dispersion model (LPDM). The new scaling approach is introduced to the interface between COSMO and LPDM, and is compared to results of a classical similarity theory approach and to the operational coupling type, which uses the turbulent kinetic energy (TKE) from the COSMO model directly. For the validation of the modelling system, the TRANSALP-89 tracer experiment is used, which was conducted in highly complex terrain in southern Switzerland. The ability of the COSMO model to simulate the valley wind system is assessed with several meteorological surface stations, and the dispersion simulation is evaluated with the measurements from 25 surface samplers. The sensitivity of the modelling system towards the soil moisture, horizontal grid resolution, and boundary-layer height determination is investigated, and it is shown that, if the flow field is correctly reproduced, the new scaling approach improves the tracer concentration simulation when compared to classical coupling methods.

Keywords

Atmospheric dispersion Complex terrain COSMO numerical model Lagrangian particle dispersion model Tracer experiment Turbulence parameterization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ambrosetti P, Anfossi D, Cieslik S, Graziani G, Lamprecht R, Marzorati A, Nodop K, Sandroni S, Stingele A, Zimmermann H (1998) Mesoscale transport of atmospheric trace constituents across the central Alps: TRANSALP tracer experiments. Atmos Environ 32: 1257–1272CrossRefGoogle Scholar
  2. Anfossi D, Desiato F, Tinarelli G, Brusasca G, Ferrero E, Sacchetti D (1998) TRANSALP 1989 experimental campaign–II. Simulation of a tracer experiment with Lagrangian particle models. Atmos Environ 32: 1157–1166CrossRefGoogle Scholar
  3. Arpagaus M (2005) Verification of vertical profiles: Operational verification at MeteoSwiss. COSMO Newsl 5:102–105. http://www.cosmo-model.org
  4. Astrup P, Mikkelsen T, Deme S (2001) METRODOS: meteorological preprocessor chain. Phys Chem Earth B 26: 105–110Google Scholar
  5. Batchelor GK (1949) Diffusion in a field of homogeneous turbulence. I. Eulerian analysis. Aust J Sci Res 2: 437–450Google Scholar
  6. Buzzi M (2008) Challenges in operational numerical weather prediction at high resolution in complex terrain. Dissertation No. 17714, ETH Zürich, 197 ppGoogle Scholar
  7. Buzzi M, Rotach MW, Raschendorfer M, Holtslag AAM (2010) Evaluation of the COSMO-SC boundary layer scheme for stable conditions. Meteorol Z (in press)Google Scholar
  8. Carvalho JC, Anfossi D, Trini Castelli S, Degrazia GA (2002) Application of a model system for the study of transport and diffusion in complex terrain to the TRACT experiment. Atmos Environ 36: 1147–1161CrossRefGoogle Scholar
  9. Chow FK, Weigel AP, Street RL, Rotach MW, Xue M (2006) High-resolution large-eddy simulations of flow in a steep Alpine valley. Part I: methodology, verification, and sensitivity experiments. J Appl Meteorol 45: 63–86CrossRefGoogle Scholar
  10. De Wekker SFJ, Steyn DG, Fast JD, Rotach MW, Zhong S (2005) The performance of RAMS in representing the convective boundary layer structure in a very steep valley. Environ Fluid Mech 5: 35–62CrossRefGoogle Scholar
  11. Desiato F, Finardi S, Brusasca G, Morselli MG (1998) TRANSALP 1989 experimental campaign—I. Simulation of 3D flow with diagnostic wind field models. Atmos Environ 32: 1141–1156CrossRefGoogle Scholar
  12. Doms G, Schaettler U (2002) The nonhydrostatic limited-area model LM–part I: dynamics and numerics. Scientific Documentation, Deutscher Wetterdienst, Offenbach, Germany, 140 pp. http://www.cosmo-model.org
  13. Enger L, Koracin D (1995) Simulations of dispersion in complex terrain using a higher order closure model. Atmos Environ 29: 2449–2456CrossRefGoogle Scholar
  14. Enger L, Koracin D, Yang X (1993) A numerical study of boundary-layer dynamics in a mountain valley. Part 1: model validation and sensitivity experiments. Boundary-Layer Meteorol 66: 357–394CrossRefGoogle Scholar
  15. Fiedler F (1989) EUREKA Environmental Project—EUROTRAC, proposal of a subproject. Transport of Air Pollutants over Complex Terrain (TRACT), KarlsruheGoogle Scholar
  16. Folini D, Ubl S, Kaufmann P (2008) Lagrangian particle dispersion modeling for the high Alpine site Jungfraujoch. J Geophys Res 113: D18111CrossRefGoogle Scholar
  17. Gantner L, Kalthoff N (2010) Sensitivity of a modelled life cycle of a mesoscale convective system to soil conditions over West Africa. Q J Roy Meteorol Soc 136: 471–482CrossRefGoogle Scholar
  18. Glaab H, Fay B, Jacobsen I (1998) Evaluation of the emergency dispersion model at the Deutscher Wetterdienst using ETEX data. Atmos Environ 32: 4359–4366CrossRefGoogle Scholar
  19. Hanna SR (1982) Applications in air pollution modeling. In: Nieuwstadt FTM, van Dop H (eds) Atmospheric turbulence and air pollution modelling. D. Reidel Publishing Company, Dordrecht, pp 275–310Google Scholar
  20. Hanna SR (1989) Confidence limit for air quality models as estimated by bootstrap and jacknife resampling methods. Atmos Environ 23: 1385–1395CrossRefGoogle Scholar
  21. Hara T, Trini Castelli S, Ohba R, Tremback CJ (2009) Validation studies of turbulence closure schemes for high resolutions in mesoscale meteorological models—a case of gas dispersion at the local scale. Atmos Environ 43: 3745–3753CrossRefGoogle Scholar
  22. Heimann D, de Franceschi M, Emeis S, Lercher P, Seibert P (eds) (2008) Air Pollution, Traffic Noise and Related Health Effects in the Alpine space: a guide for authorities and consulters, ALPNAP comprehensive report. Università degli Studi di Trento, Dipartimento di Ingegneria Civile e Ambientale, Trento, Italy, 335 ppGoogle Scholar
  23. Kaufmann P (2005) Verification of aLMo with SYNOP and GPS data over Europe. COSMO Newsl 5:113–117. http://www.cosmo-model.org
  24. Kolmogorov AN (1941) The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Doklady ANSSSR 30: 301–304Google Scholar
  25. Legg BJ, Raupach M (1982) Markov-chain simulation of particle dispersion in inhomogeneous flows: the mean drift velocity induced by a gradient in Eulerian velocity variance. Boundary-Layer Meteorol 24: 3–13CrossRefGoogle Scholar
  26. Mellor GL, Yamada T (1974) A hierarchy of turbulence closure models for planetary boundary layers. J Atmos Sci 31: 1791–1806CrossRefGoogle Scholar
  27. Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical flow problems. Rev Geophys Space Phys 20: 851–875CrossRefGoogle Scholar
  28. Michioka T, Chow FK (2008) High-resolution large-eddy simulations of scalar transport in atmospheric boundary layer flow over complex terrain. J Appl Meteorol 47: 3150–3169CrossRefGoogle Scholar
  29. Müller MD, Scherrer D (2005) A grid and subgrid scale radiation parameterization of topographic effects for mesoscale weather forecast models. Mon Weather Rev 133: 1431–1442CrossRefGoogle Scholar
  30. Readings CJ, Haugen DA, Kaimal JC (1974) The 1973 Minnesota atmospheric boundary layer experiment. Weather 29: 309–312Google Scholar
  31. Rotach MW, Calanca P, Graziani P, Gurtz J, Steyn DG, Vogt R, Andretta M, Christen A, Cieslik S, Connolly R, De Wekker SFJ, Galmarini S, Kadygrov EN, Kadygrov V, Miller E, Neininger B, Rucker M, van Gorsel E, Weber H, Weiss A, Zappa M (2004) Turbulence structure and exchange processes in an Alpine Valley: the Riviera project. Bull Am Meteorol Soc 85: 1367–1385CrossRefGoogle Scholar
  32. Rotta JC (1951a) Statistische Theorie nichthomogener Turbulenz. Z Phys 129: 547–572CrossRefGoogle Scholar
  33. Rotta JC (1951b) Statistische Theorie nichthomogener Turbulenz. Z Phys 131: 51–77CrossRefGoogle Scholar
  34. Schicker I, Seibert P (2009) Simulation of the meteorological conditions during a winter smog episode in the Inn Valley. Meteorol Atmos Phys 103: 211–222CrossRefGoogle Scholar
  35. Schrodin R, Heise E (2001) The multi-layer version of the soil model TERRA_LM. Consortium for Small-Scale Modelling (COSMO), Technical Report 2, 17 pp. http://www.cosmo-model.org
  36. Seibert P, Beyrich F, Gryning SE, Joffre S, Rasmussen A, Tercier P (2000) Review and intercomparison of operational methods for the determination of the mixing height. Atmos Environ 34: 1001–1027CrossRefGoogle Scholar
  37. Simmons A, Uppala S, Dee D, Kobayashi S (2007) ERA-Interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsl 110: 25–35Google Scholar
  38. Smagorinsky J (1963) General circulation experiments with the primitive equations: I. The basic experiment. Mon Weather Rev 91: 99–164CrossRefGoogle Scholar
  39. Sørensen JH, Rasmussen A, Svensmark H (1996) Forecast of atmospheric boundary-layer height utilised for ETEX real-time dispersion modeling. Phys Chem Earth 21: 435–439CrossRefGoogle Scholar
  40. Stohl A, Forster C, Frank A, Seibert P, Wotawa G (2005) Technical note: the Lagrangian particle dispersion model FLEXPART version 6.2. Atmos Chem Phys 5: 2461–2474CrossRefGoogle Scholar
  41. Szintai B, Kaufmann P (2008) TKE as a measure of turbulence. COSMO Newsl 8:2–10. http://www.cosmo-model.org Google Scholar
  42. Szintai B, Kaufmann P, Rotach MW (2009) Deriving turbulence characteristics from the COSMO numerical weather prediction model for dispersion applications. Adv Sci Res 3: 79–84CrossRefGoogle Scholar
  43. Thomson DJ (1987) Criteria for the selection of stochastic models of particle trajectories in turbulent flows. J Fluid Mech 180: 529–556CrossRefGoogle Scholar
  44. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117: 1779–1800CrossRefGoogle Scholar
  45. Trini Castelli S, Anfossi D (1997) Intercomparison of 3-D turbulence parameterizations for dispersion models in complex terrain derived from a circulation model. Il Nuovo Cimento 20: 287–313Google Scholar
  46. Trini Castelli S, Hara T, Ohba R, Tremback CJ (2006) Validation studies of turbulence closure schemes for high resolutions in mesoscale meteorological models. Atmos Environ 40: 2510–2523CrossRefGoogle Scholar
  47. Trini Castelli S, Belfiore G, Anfossi D, Elampe E, Clemente M (2007) Modelling the meteorology and traffic pollutant dispersion in highly complex terrain: the ALPNAP Alpine Space Project. In: Proceedings of the 11th international conference on Harmonisation within atmospheric dispersion modelling for regulatory purposes, vol 1, Cambridge, UK, 2–5 July 2007, pp 225–229Google Scholar
  48. Uppala S, Dee D, Kobayashi S, Berrisford P, Simmons A (2008) Towards a climate data assimilation system: status update of ERA-Interim. ECMWF Newsl 115: 12–18Google Scholar
  49. Weigel AP, Rotach MW (2004) Flow structure and turbulence characteristics of the daytime atmosphere in a steep and narrow Alpine valley. Q J Roy Meteorol Soc 130: 2605–2627CrossRefGoogle Scholar
  50. Weigel AP, Chow FK, Rotach MW (2007) On the nature of turbulent kinetic energy in a steep and narrow Alpine valley. Boundary-Layer Meteorol 123: 177–199CrossRefGoogle Scholar
  51. Zängl G, Chimani B, Häberli C (2004) Numerical simulations of the foehn in the Rhine Valley on 24 October 1999 (MAP IOP 10). Mon Weather Rev 132: 368–389CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Federal Office of Meteorology and Climatology MeteoSwissZurichSwitzerland
  2. 2.Hungarian Meteorological ServiceBudapestHungary
  3. 3.Institute for Meteorology and GeophysicsUniversity of InnsbruckInnsbruckAustria

Personalised recommendations