The aim of this paper is essentially twofold: first, to describe the
use of spherical nonparametric estimators for determining statistical
diagnostic fields from ensembles of feature tracks on a global domain,
and second, to report the application of these techniques to data
derived from a modern general circulation model. New spherical kernel
functions are introduced that are more efficiently computed than
the traditional exponential kernels. The data-driven techniques of
cross-validation to determine the amount elf smoothing objectively,
and adaptive smoothing to vary the smoothing locally, are also considered.
Also introduced are techniques for combining seasonal statistical
distributions to produce longer-term statistical distributions. Although
all calculations are performed globally, only the results for the
Northern Hemisphere winter (December, January, February) and Southern
Hemisphere winter (June, July, August) cyclonic activity are presented,
discussed, and compared with previous studies. Overall, results for
the two hemispheric winters are in good agreement with previous studies,
both for model-based studies and observational studies
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