气候变化领域集成服务门户(CCPortal): Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations

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DOI	10.1016/j.atmosenv.2020.117313
	Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations
	Girard S.; Armand P.; Duchenne C.; Yalamas T.
发表日期	2020
ISSN	13522310
卷号	224
英文摘要	Decision of emergency response to releases of hazardous material in the atmosphere increasingly rely on numerical simulations. This paper presents two contributions for accounting for the uncertainty inherent to those simulations. We first focused on one way of modelling these uncertainties, namely by applying stochastic perturbations to the inputs of the numerical dispersion model. We devised a generic mathematical formulation for time dependent perturbation of both amplitude and dynamics of the inputs. It allows a more thorough exploration of possible outcomes than simpler perturbations found in the literature. We then improved on the current state of the art on dimension reduction of atmospheric data. Indeed, most statistical methods cannot cope with high dimensional data such as the maps simulated with atmospheric dispersion models. Principal component analysis, the most widely used method for dimension reduction, relies on a linearity hypothesis that is not verified by these sets of maps. We conducted a very encouraging experiment with auto-associative models, a non-linear extension of this method. © 2020 Elsevier Ltd
英文关键词	Atmospheric dispersion; Perturbation; Time warp; Uncertainty propagation; Wind field
学科领域	Clustering algorithms; Dispersion (waves); Principal component analysis; Stochastic models; Stochastic systems; Uncertainty analysis; Atmospheric dispersion; Perturbation; Time Warp; Uncertainty propagation; Wind field; Atmospheric movements; dispersion; modeling; perturbation; simulation; stochasticity; wind field; article; atmospheric dispersion; principal component analysis; simulation; stochastic model; uncertainty
语种	英语
scopus关键词	Clustering algorithms; Dispersion (waves); Principal component analysis; Stochastic models; Stochastic systems; Uncertainty analysis; Atmospheric dispersion; Perturbation; Time Warp; Uncertainty propagation; Wind field; Atmospheric movements; dispersion; modeling; perturbation; simulation; stochasticity; wind field; article; atmospheric dispersion; principal component analysis; simulation; stochastic model; uncertainty
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120740
作者单位	18 boulevard de Reuilly, Phimeca, France; CEA, DAM, DIF, Arpajon, F-91297, France; Centre d'affaires du Zénith, 34 rue de Sarliève, Cournon d'Auvergne, 63800, France
推荐引用方式 GB/T 7714	Girard S.,Armand P.,Duchenne C.,et al. Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations[J],2020,224.
APA	Girard S.,Armand P.,Duchenne C.,&Yalamas T..(2020).Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations.Atmospheric Environment,224.
MLA	Girard S.,et al."Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations".Atmospheric Environment 224(2020).