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• W2298227986 abstract "Abstract. Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g. concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation allows to estimate also all tuning parameters from the measurements. The estimation procedure is based on the Variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to estimate also all tuning parameters from the observations. The proposed algorithm is tested and compared with the state-of-the-art method on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download." @default.
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• W2298227986 date "2016-03-17" @default.
• W2298227986 modified "2023-09-25" @default.
• W2298227986 title "A tuning-free method for the linear inverse problem and its application to source term determination" @default.
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• W2298227986 doi "https://doi.org/10.5194/gmd-2016-5" @default.
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