Statistical inference for atmospheric transport models using process convolutions
Identifieur interne : 001142 ( Main/Exploration ); précédent : 001141; suivant : 001143Statistical inference for atmospheric transport models using process convolutions
Auteurs : Weining Zhou [États-Unis] ; Bruno Sans [États-Unis]Source :
- Environmetrics [ 1180-4009 ] ; 2008-02.
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Abstract
A computer simulator for atmospheric concentrations of chemical species, or chemical transport model, is used to simulate global ozone concentrations. Two different wind forcings are considered: one is a combination of a numerical weather prediction model and observational data, the other is obtained as output from a climate model. The goal is to study the impact of meteorological variability on ozone. The statistical approach that we consider consists on learning the spatial structure of ozone concentrations by using process convolutions. We use several Bayesian model comparison methods to determine if the two simulations can be considered as realizations of the same random field. The methods provide a quantification of the differences for each of the computer model grid cells. Copyright © 2007 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/env.858
Affiliations:
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