Matérn-based nonstationary cross-covariance models for global processes

Handle URI:
http://hdl.handle.net/10754/598764
Title:
Matérn-based nonstationary cross-covariance models for global processes
Authors:
Jun, Mikyoung
Abstract:
Many spatial processes in environmental applications, such as climate variables and climate model errors on a global scale, exhibit complex nonstationary dependence structure, in not only their marginal covariance but also their cross-covariance. Flexible cross-covariance models for processes on a global scale are critical for an accurate description of each spatial process as well as the cross-dependences between them and also for improved predictions. We propose various ways to produce cross-covariance models, based on the Matérn covariance model class, that are suitable for describing prominent nonstationary characteristics of the global processes. In particular, we seek nonstationary versions of Matérn covariance models whose smoothness parameters vary over space, coupled with a differential operators approach for modeling large-scale nonstationarity. We compare their performance to the performance of some existing models in terms of the aic and spatial predictions in two applications: joint modeling of surface temperature and precipitation, and joint modeling of errors in climate model ensembles. © 2014 Elsevier Inc.
Citation:
Jun M (2014) Matérn-based nonstationary cross-covariance models for global processes. Journal of Multivariate Analysis 128: 134–146. Available: http://dx.doi.org/10.1016/j.jmva.2014.03.009.
Publisher:
Elsevier BV
Journal:
Journal of Multivariate Analysis
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jul-2014
DOI:
10.1016/j.jmva.2014.03.009
Type:
Article
ISSN:
0047-259X
Sponsors:
Mikyoung Jun's research was supported by NSF grant DMS-1208421. This publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The author acknowledges the modeling groups for making their simulations available for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output, and the World Climate Research Programme (WCRP)'s Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multi-model data set is supported by the Office of Science, U.S. Department of Energy. The author thanks the Editor, the Associate Editor, and two anonymous reviewers whose comments helped to improve the paper significantly.
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Full metadata record

DC FieldValue Language
dc.contributor.authorJun, Mikyoungen
dc.date.accessioned2016-02-25T13:40:45Zen
dc.date.available2016-02-25T13:40:45Zen
dc.date.issued2014-07en
dc.identifier.citationJun M (2014) Matérn-based nonstationary cross-covariance models for global processes. Journal of Multivariate Analysis 128: 134–146. Available: http://dx.doi.org/10.1016/j.jmva.2014.03.009.en
dc.identifier.issn0047-259Xen
dc.identifier.doi10.1016/j.jmva.2014.03.009en
dc.identifier.urihttp://hdl.handle.net/10754/598764en
dc.description.abstractMany spatial processes in environmental applications, such as climate variables and climate model errors on a global scale, exhibit complex nonstationary dependence structure, in not only their marginal covariance but also their cross-covariance. Flexible cross-covariance models for processes on a global scale are critical for an accurate description of each spatial process as well as the cross-dependences between them and also for improved predictions. We propose various ways to produce cross-covariance models, based on the Matérn covariance model class, that are suitable for describing prominent nonstationary characteristics of the global processes. In particular, we seek nonstationary versions of Matérn covariance models whose smoothness parameters vary over space, coupled with a differential operators approach for modeling large-scale nonstationarity. We compare their performance to the performance of some existing models in terms of the aic and spatial predictions in two applications: joint modeling of surface temperature and precipitation, and joint modeling of errors in climate model ensembles. © 2014 Elsevier Inc.en
dc.description.sponsorshipMikyoung Jun's research was supported by NSF grant DMS-1208421. This publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The author acknowledges the modeling groups for making their simulations available for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output, and the World Climate Research Programme (WCRP)'s Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multi-model data set is supported by the Office of Science, U.S. Department of Energy. The author thanks the Editor, the Associate Editor, and two anonymous reviewers whose comments helped to improve the paper significantly.en
dc.publisherElsevier BVen
dc.subjectClimate model outputen
dc.subjectCross-covariance modelen
dc.subjectGlobal processen
dc.subjectMatérn covariance functionen
dc.subjectNonstationary processen
dc.titleMatérn-based nonstationary cross-covariance models for global processesen
dc.typeArticleen
dc.identifier.journalJournal of Multivariate Analysisen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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