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Assimilation de données et couplage d'échelles pour la simulation de la dispersion atmosphérique en milieu urbain

Abstract : Air quality monitoring is currently carried out with concentration measurements and with atmospheric dispersion modeling tools. These numerical models evaluate pollutant concentrations with a finer spatio-temporal resolution than measurements. Nevertheless, the estimates provided by these models are less accurate than measurements. In this research project, we studied multiscale coupling and data assimilation approaches to improve the estimates provided by the SIRANE atmospheric dispersion model, dedicated to the urban scale. The multiscale coupling approach consists in determining the boundary conditions of a simulation from another simulation on a larger scale. In this thesis work, we analyzed three methods for coupling the SIRANE model with the CHIMERE mesoscale model. This study shows that these methods can potentially estimate the air quality at the urban scale more satisfactorily than the mesoscale models (used alone). However, they do not necessarily improve the modeling of the boundary conditions of a simulation at the urban scale and the estimates provided by them. This is a priori due to the fact that the estimates provided by the CHIMERE model are not sufficiently good on our case study. It is possible, however, that these methods improve the results at the urban scale by using a better simulation at the regional scale. The data assimilation approach consists of combining the measurements and the modelled data to determine the best estimate of the system state. During this thesis, we studied three data assimilation methods : the unbiased method, the method that we called source apportionment modulation, and the Best Linear Unbiased Estimator method. This study indicates that these methods generally improve the estimates provided by the SIRANE model. The sensitivity study on the number of measurements used during the data assimilation indicates that, in general, higher is this number, more satisfactory are the results. Finally, the results show that the statistical performances associated with these three data assimilation methods are globally comparable on our case study.
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Submitted on : Tuesday, May 19, 2020 - 6:35:16 AM
Last modification on : Wednesday, July 8, 2020 - 12:42:09 PM


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  • HAL Id : tel-02611980, version 2


Chi Vuong Nguyen. Assimilation de données et couplage d'échelles pour la simulation de la dispersion atmosphérique en milieu urbain. Autre. Université de Lyon, 2017. Français. ⟨NNT : 2017LYSEC018⟩. ⟨tel-02611980v2⟩



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