  The gaussian spread regression model for the calibration of site specific
ensemble temperature forecasts depends on the apparently restrictive assumption
that the uncertainty around temperature forecasts is normally distributed. We
generalise the model using the kernel density to allow for much more flexible
distribution shapes. However, we do not find any meaningful improvement in the
resulting probabilistic forecast when evaluated using likelihood based scores.
We conclude that the distribution of uncertainty is either very close to
normal, or if it is not close to normal, then the non-normality is not being
predicted by the ensemble forecast that we test.
