Siegert et al "On the predictability of outliers in ensemble forecasts"

Siegert, S., BrÃ¶cker, J., and Kantz, H. On the predictability of outliers in
ensemble forecasts. Advances in Science and Research 8 (pp. 53-57), 2012.

In numerical weather prediction, ensembles are used to retrieve probabilistic
forecasts of future weather conditions. We consider events where the
verification is smaller than the smallest, or larger than the largest ensemble
member of a scalar ensemble forecast. These events are called outliers. In a
statistically consistent K-member ensemble, outliers should occur with a base
rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We
study the predictability of outlier events in terms of the Brier Skill Score
and find that forecast probabilities can be calculated which are more skillful
than the unconditional base rate. This is shown analytically for statistically
consistent ensembles. Using logistic regression, forecast probabilities for
outlier events in an operational ensemble are calculated. These probabilities
exhibit positive skill which is quantitatively similar to the analytical
results. Possible causes of these results as well as their consequences for
ensemble interpretation are discussed.