  We describe various moment-based ensemble interpretation models for the
construction of probabilistic temperature forecasts from ensembles. We apply
the methods to one year of medium range ensemble forecasts and perform in and
out of sample testing. Our main conclusion is that probabilistic forecasts
derived from the ensemble mean using regression are just as good as those based
on the ensemble mean and the ensemble spread using a more complex calibration
algorithm. The explanation for this seems to be that the predictable component
of the variability of the forecast uncertainty is only a small fraction of the
total forecast uncertainty. Users of ensemble temperature forecasts are
advised, until further evidence becomes available, to ignore the ensemble
spread and build probabilistic forecasts based on the ensemble mean alone.
