  We describe results from the second stage of a project to build a statistical
model for hurricane tracks. In the first stage we modelled the unconditional
mean track. We now attempt to model the unconditional variance of fluctuations
around the mean. The variance models we describe use a semi-parametric nearest
neighbours approach in which the optimal averaging length-scale is estimated
using a jack-knife out-of-sample fitting procedure. We test three different
models. These models consider the variance structure of the deviations from the
unconditional mean track to be isotropic, anisotropic but uncorrelated, and
anisotropic and correlated, respectively. The results show that, of these
models, the anisotropic correlated model gives the best predictions of the
distribution of future positions of hurricanes.
