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Aggregation-cokriging for highly multivariate spatial data


Furrer, R; Genton, M G (2011). Aggregation-cokriging for highly multivariate spatial data. Biometrika, 98(3):615-631.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:April 2011
Deposited On:17 Feb 2012 19:51
Last Modified:22 Sep 2018 07:04
Publisher:Oxford University Press
ISSN:0006-3444 (P) 1464-3510 (E)
OA Status:Green
Publisher DOI:https://doi.org/10.1093/biomet/asr029

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