Matern Cross-Covariance Functions for Multivariate Random Fields

Tech Report Number
549

Abstract

We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Mat´ern process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and co-located correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance functions are of the Mat´ern type. In a data example on error fields for numerical predictions of surface pressure and temperature over the Pacific Northwest, a parsimonious bivariate Mat´ern model compares favorably to the traditional linear model of coregionalization.

Keywords: co-kriging; convolution; cross-correlation; Mat´ern class; Gaussian spatial random field; maximum likelihood; multivariate geostatistics; numerical weather prediction; positive definite

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