Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 128, Issue -, Pages 134-146Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2014.03.009
Keywords
Climate model output; Cross-covariance model; Global process; Matern covariance function; Nonstationary process
Categories
Funding
- NSF [DMS-1208421]
- King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]
- Office of Science, U.S. Department of Energy
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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 Matern covariance model class, that are suitable for describing prominent nonstationary characteristics of the global processes. In particular, we seek nonstationary versions of Matern 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. (C) 2014 Elsevier Inc. All rights reserved.
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