Aggregation-cokriging for highly multivariate spatial data

R. Furrer, M. G. Genton

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.
Original languageEnglish (US)
Pages (from-to)615-631
Number of pages17
JournalBiometrika
Volume98
Issue number3
DOIs
StatePublished - Aug 26 2011
Externally publishedYes

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