  We discuss how the kernel convolution approach can be used to accurately
approximate the spatial covariance model on a sphere using spherical distances
between points. A detailed derivation of the required formulas is provided. The
proposed covariance model approximation can be used for non-stationary spatial
prediction and simulation in the case when the dataset is large and the
covariance model can be estimated separately in the data subsets.
