Statistical Science

Spherical Process Models for Global Spatial Statistics

Jaehong Jeong, Mikyoung Jun, and Marc G. Genton

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Abstract

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.

Article information

Source
Statist. Sci., Volume 32, Number 4 (2017), 501-513.

Dates
First available in Project Euclid: 28 November 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1511838025

Digital Object Identifier
doi:10.1214/17-STS620

Mathematical Reviews number (MathSciNet)
MR3730519

Zentralblatt MATH identifier
1381.62091

Keywords
Axial symmetry chordal distance geodesic distance nonstationarity smoothness sphere

Citation

Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G. Spherical Process Models for Global Spatial Statistics. Statist. Sci. 32 (2017), no. 4, 501--513. doi:10.1214/17-STS620. https://projecteuclid.org/euclid.ss/1511838025


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