Data assimilation in geophysical sciences aims at optimally estimating the state of the system or some parameters of the system's physical model. To do so, data assimilation needs three types of information: observations and background information, a physical/numerical model, and some statistical description that prescribes uncertainties to each componenent of the system.In my dissertation, new methodologies of data assimilation are used in atmospheric chemistry and physics: the joint use of a 4D-Var with a subgrid statistical model to consistently account for representativeness errors, accounting for multiple scale in the BLUE estimation principle, and a better estimation of prior errors using objective estimation of hyperparameters. These three approaches will be specifically applied to inverse modelling problems focussing on the emission fields of tracers or pollutants. First, in order to estimate the emission inventories of carbon monoxide over France, in-situ stations which are impacted by the representativeness errors are used. A subgrid model is introduced and coupled with a 4D-Var to reduce the representativeness error. Indeed, the results of inverse modelling showed that the 4D-Var routine was not fit to handle the representativeness issues. The coupled data assimilation system led to a much better representation of theCO concentration variability, with a significant improvement of statistical indicators, and more consistent estimation of the CO emission inventory. Second, the evaluation of the potential of the IMS (International Monitoring System) radionuclide network is performed for the inversion of an accidental source. In order to assess the performance of the global network, a multiscale adaptive grid is optimised using a criterion based on degrees of freedom for the signal (DFS). The results show that several specific regions remain poorly observed by the IMS network. Finally, the inversion of the surface fluxes of Volatile Organic Compounds (VOC) are carried out over Western Europe using EMEP stations. The uncertainties of the background values of the emissions, as well as the covariance matrix of the observation errors, are estimated according to the maximum likelihood principle. The prior probability density function of the control parameters is chosen to be Gaussian or semi-normal distributed. Grid-size emission inventories are inverted under these two statistical assumptions. The two kinds of approaches are compared. With the Gaussian assumption, the departure between the posterior and the prior emission inventories is higher than when using the semi-normal assumption, but that method does not provide better scores than the semi-normal in a forecast experiment.
| Identifer | oai:union.ndltd.org:CCSD/oai:pastel.archives-ouvertes.fr:pastel-00807468 |
| Date | 20 December 2012 |
| Creators | Koohkan, Mohammad Reza, Koohkan, Mohammad Reza |
| Publisher | Université Paris-Est |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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