  With the widespread availability of satellite-based instruments, many
geophysical processes are measured on a global scale and they often show strong
nonstationarity in the covariance structure. In this paper we present a
flexible class of parametric covariance models that can capture the
nonstationarity in global data, especially strong dependency of covariance
structure on latitudes. We apply the Discrete Fourier Transform to data on
regular grids, which enables us to calculate the exact likelihood for large
data sets. Our covariance model is applied to global total column ozone level
data on a given day. We discuss how our covariance model compares with some
existing models.
