Air Quality Ensemble Forecast Coupling ARPEGE and CHIMERE over Western Europe

  • Ana C. Carvalho
  • Laurent Menut
  • Robert Vautard
  • Jean Nicolau
Conference paper
Part of the NATO Science for Peace and Security Series Series C: Environmental Security book series (NAPSC)


The quality enhancement of the results encountered on numerical weather prediction ensemble runs has encouraged the air quality modellers’ community to test the same methodology to foresee air pollutants concentrations in the atmosphere. In air quality forecast it is important to know in advance if the event exceedences of a certain threshold value will happen in order to implement mitigation measures concerning air pollutant emission. The ensemble approach allows giving this information within a probability range.

Within this work both perturbation on the circulation model and the chemical transport model will be implemented. The ensemble system is composed by the numerical weather prediction model ARPEGE, the meteorological model MM5 and the chemical transport model CHIMERE. Meteorological perturbations will be addressed firstly by a set of ten ensemble members derived by the ARPEGE model, plus a control run, which will force MM5 simulations. Since the concept of air pollution ensemble forecast is not the same than the one for meteorology, we propose here an original approach for the chemistry-transport model perturbations based on previously done CHIMERE sensitivities studies: focus will be made on plausible emissions scenarios and different daily emissions profiles, boundary layer evolution, vertical mixing and photolysis rates. The ensemble based simulations will cover July and August 2006, each includes the heat wave period that influenced the weather and air quality conditions of central Europe.


Chemical ensemble forecast ozone 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Chiriaco M, Vautard R, Chepfer H, Haeffelin M, Wanherdrick Y, Morille Y, Protat A, Dudhia J (2005) The ability of MM5 to simulate Ice clouds: systematic comparison between simulated and measured fluxes and lidar/radar profiles at SIRTA atmospheric observatory. Month. Wea. Rev., 134, 897-918.CrossRefGoogle Scholar
  2. Dabberdt WF, Miller E (2000) Uncertainty, ensembles and air quality dispersion modeling: applications and challenges, Atmos. Environ., 34, 4667-4673.CrossRefGoogle Scholar
  3. Delle Monache L, Deng X, Zhou Y, Stull R (2006) Ozone ensemble forecasts: 1. A new ensemble design, J. Geophys. Res., 111, D05307, doi:10.1029/ 2005JD006310.CrossRefGoogle Scholar
  4. Dudhia J (1993) A nonhydrostatic version of the Penn State - NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front. Month. Wea. Rev., 121, 1493-1513.CrossRefGoogle Scholar
  5. Galmarini S et al.(2004) Ensemble dispersion forecasting- Part I: concept, approach and indicators, Atmos. Environ., 38, 4607-4617.CrossRefGoogle Scholar
  6. Hamill TM, Mullen SL, Snyder C, Toth Z, Baumhefner DP (2000) Ensemble Forecasting in the Short to Medium Range: Report from a Workshop, Bull. Am. Meteorol. Soc., 81, 2653-2664.CrossRefGoogle Scholar
  7. Honoré C et al. Predictability of regional air quality in Europe: the assessment of three years of operational forecasts and analyses over France, J. Geophys. Res., under revision.Google Scholar
  8. IPCC (2006) IPCC Guidelines for National Greenhouse Gas Inventories (
  9. Kühlwein J, Friedrich R (2000) Uncertainties of modelling emissions from road transport. Atmos. Environ. 34, 4603-4610.CrossRefGoogle Scholar
  10. Lorenz EN (1963) Deterministic non-periodic flow. J. Atmos. Sci., 20, 130-141.CrossRefGoogle Scholar
  11. Menut L (2003) Adjoint modeling for atmospheric pollution process sensitivity at regional scale, J. Geophys. Res., 108(D17), 8562, doi:10.1029/2002JD002549.CrossRefGoogle Scholar
  12. Theloke J, Friedrich R (2002) NMVOC Emissions from Solvent Use in Germany 2000. Annual Report 2001 of the EUROTRAC subproject Generation and evaluation of emission data - GENEMIS. Munich 2002.Google Scholar
  13. Vautard R, Beekmann M, Roux J, Gombert D (2001) Validation of a deterministic forecasting system for the ozone concentrations over the Paris area. Atmos. Environ., 35, 2449-2461.CrossRefGoogle Scholar
  14. Vautard R et al. (2006). Is regional air quality model diversity representative of uncertainty for ozone simulation? Geophys. Res. Lett., 33, L24818, doi:10.1029/2006GL027610. URL 1: URL 2:

Copyright information

© Springer Science + Business Media B.V 2008

Authors and Affiliations

  • Ana C. Carvalho
    • 1
  • Laurent Menut
    • 2
  • Robert Vautard
    • 3
  • Jean Nicolau
  1. 1.Laboratoire de Météorologie DynamiqueÉcole PolytechniquePalaiseauFrance
  2. 2.Laboratoire de Météorologie DynamiqueIPSL/CNRS École PolytechniquePalaiseauFrance
  3. 3.LSCE/IPSL – Laboratoire CEA/CNRS/UVSQGif Sur YvetteFrance

Personalised recommendations