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Predicting outliers in ensemble forecasts

Siegert, S., Bröcker, J. and Kantz, H. (2011) Predicting outliers in ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 137 (660). pp. 1887-1897. ISSN 1477-870X

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To link to this item DOI: 10.1002/qj.868


An ensemble forecast is a collection of runs of a numerical dynamical model, initialized with perturbed initial conditions. In modern weather prediction for example, ensembles are used to retrieve probabilistic information about future weather conditions. In this contribution, we are concerned with ensemble forecasts of a scalar quantity (say, the temperature at a specific location). We consider the event that the verification is smaller than the smallest, or larger than the largest ensemble member. We call these events outliers. If a K-member ensemble accurately reflected the variability of the verification, outliers should occur with a base rate of 2/(K + 1). In operational forecast ensembles though, this frequency is often found to be higher. We study the predictability of outliers and find that, exploiting information available from the ensemble, forecast probabilities for outlier events can be calculated which are more skilful than the unconditional base rate. We prove this analytically for statistically consistent forecast ensembles. Further, the analytical results are compared to the predictability of outliers in an operational forecast ensemble by means of model output statistics. We find the analytical and empirical results to agree both qualitatively and quantitatively.

Item Type:Article
Divisions:No Reading authors. Back catalogue items
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:29160
Uncontrolled Keywords:forecast probability, predictability, Talagrand diagrams, ROC curves, skill scores
Publisher:Royal Meteorological Society

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