Air quality modeling: From deterministic to stochastic approaches

Abstract : The objective of this article is to investigate the topics related to uncertainties in air quality modeling. A first point is the evaluation of uncertainties for model outputs: Monte Carlo methods and sensitivity analysis are powerful methods for assessing the impact of uncertainties due to model inputs. A second point is devoted to ensemble modeling with multi-models approaches. According to the wide spread in the model outputs, using a unique model, tuned to a small set of observational data, is not relevant in this field. On the basis of ensemble simulations, improved forecasts are given by appropriate algorithms to combine the set of models. The results applied to air quality modeling at continental scale with the Polyphemus system illustrate these methods. The first estimates of uncertainties in inverse modeling experiments are also proposed.
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Computers and Mathematics with Applications, Elsevier, 2008, 55 (10), pp.2329--2337. 〈10.1016/j.camwa.2007.11.004〉
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https://hal.inria.fr/inria-00581301
Contributeur : Nathalie Gaudechoux <>
Soumis le : mercredi 30 mars 2011 - 15:54:20
Dernière modification le : jeudi 4 janvier 2018 - 17:52:01

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Vivien Mallet, Bruno Sportisse. Air quality modeling: From deterministic to stochastic approaches. Computers and Mathematics with Applications, Elsevier, 2008, 55 (10), pp.2329--2337. 〈10.1016/j.camwa.2007.11.004〉. 〈inria-00581301〉

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