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DY: Fachverband Dynamik und Statistische Physik

DY 10: Posters I

DY 10.44: Poster

Monday, March 14, 2011, 17:00–19:00, P4

Predicting Outliers in Ensemble Forecasts — •Stefan Siegert, Jochen Bröcker, and Holger Kantz — Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

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), and their relation to the verification (i. e. the actual observation of that quantity). 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 reflects the variability of the verification, outliers should occur with a relative frequency 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 skillful than the unconditional relative frequency. In other words, using ensemble information, more accurate forecasts of impending outliers are possible than just stating their relative frequency. We show this analytically for statistically consistent ensembles and empirically for an operational ensemble using methods of model output statistics. Our results are relevant for evaluating and post-processing ensemble forecasts.