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Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model

Chang, KL; Guillas, S; (2019) Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model. Journal of the Royal Statistical Society - Applied Statistics: Series C , 68 (1) pp. 51-78. 10.1111/rssc.12309. Green open access

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Abstract

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation–stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach.

Type: Article
Title: Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12309
Publisher version: https://doi.org/10.1111/rssc.12309
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Gaussian process, Integrated nested Laplace approximation–stochastic partial differential equation, Matérn fields, Uncertainty quantification
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10057193
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