Parametric nonstationary covariance functions on spheres

Gaussian Processes are powerful tools for modelling spatial data. In this context, a significant amount of modelling focus is placed on specifying the covariance function, which is required to be symmetric and positive definite. Covariance functions have classically been defined and used in Euclidean space. However, as data collected from the globe becomes more prevalent, accounting for Earth's geometry becomes increasingly important. Using Euclidean distance can be suboptimal for these data. We survey the literature for recent developments related to construction of nonstationary covariance functions on spheres, which historically has been a challenging area. We present contributions in this effort by providing three general forms for families of parametric nonstationary covariance functions.

Automated summary

Gaussian Processes are powerful tools for spatial data modeling. In this work, we focus on specifying the symmetric and positive definite covariance function, which has traditionally been defined and used in Euclidean space. However, considering Earth's geometry becomes increasingly important when dealing with data collected from the globe. We survey recent developments related to constructing nonstationary covariance functions on spheres and provide three general forms for families of parametric nonstationary covariance functions.