We described the key features of the pragmatic geostatistical methodology aiming at
resolving the following drawbacks of classical geostatistical models: assuming that the data …

A fundamental decision to make during the analysis of geostatistical data is the modeling of
the spatial dependence structure as stationary or non-stationary. Although second-order …

Introducing flexible covariance functions is critical for interpolating spatial data since the
properties of interpolated surfaces depend on the covariance function used for Kriging. An …

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 …

Maximum likelihood estimation for parameter fitting given observations from a Gaussian
process in space is a computationally demanding task that restricts the use of such methods …

There have been noticeable advancements in develo** parametric covariance models for
spatial and spatio-temporal data with various applications to environmental problems …

The modeling of real-valued random fields indexed by spherical coordinates arises in
different disciplines of the natural sciences, especially in environmental, atmospheric and …

Reducing storage of global wind ensembles with stochastic generators Page 1 The Annals of
Applied Statistics 2018, Vol. 12, No. 1, 490–509 https://doi.org/10.1214/17-AOAS1105 © Institute …

Kriging models using five methods for calculating distance between locations on Earth's
surface are discussed. After reviewing statistical literature about kriging on a sphere, we …

The paper provides a way to model axially symmetric random fields defined over the two-
dimensional unit sphere embedded in the three-dimensional Euclidean space. Specifically …