Modélisation et surveillance de la qualité de l’air : de nouvelles données d’observation pour l’amélioration des modèles

Abstract : Improvement of the forecasting and mapping skills of the models implemented in the PREV’AIR system (French operational air quality forecasting platform) goes through a wider use of observation data, whatever their type (ground level or 3D in situ, satellite retrievals). With this research program held from 2006 to 2008, INERIS processed and analyzed a large set of observational 3-Dimensional data issued from networks of radiosondes, LIDARs, photometers, aircrafts and satellites. It had been focused on ozone and Particulate Matter (PM) issues. The CHIMERE chemistrytransport model developed by INERIS and CNRS, which provides the PREV’AIR system with forecasts and analyzed maps, has been evaluated against these data. Special care had been accorded to the vertical variability of the simulated pollutant concentrations which are highly dependent from the model boundary conditions. Assimilation of 3D in situ and satellite data in models for correcting the initialization fi elds of the forecasting process had also been investigated. Operational implementation of such approaches is still constrained by the availability of the data: satellite information is cloud-cover dependant and its temporal resolution is still too sparse. However it has been demonstrated that when the data are available, the approach can improve significantly the results, but this positive effect collapses few hours after the assimilation.
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Frédérik Meleux, Anthony Ung, Laurence Rouil. Modélisation et surveillance de la qualité de l’air : de nouvelles données d’observation pour l’amélioration des modèles. Rapport Scientifique INERIS, 2009, 2008-2009, pp.30-32. ⟨ineris-01869241⟩

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