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Statistical inference for atmospheric transport models using process convolutions
Author(s) -
Zhou Weining,
Sansó Bruno
Publication year - 2008
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.858
Subject(s) - bayesian inference , meteorology , inference , statistical inference , environmental science , bayesian probability , grid , computer science , mathematics , statistics , artificial intelligence , geography , geometry
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.