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Description

Statistical ensemble simulations play an important role in predicting the behavior of chaotic / noise-driven processes in nature. It is therefore imperative to understand and quantify useful information in a forecast ensemble. Modern methods of estimating predictive skill in an ensemble are often based on its mean state and variance, thus not taking into account the shape of its distribution. Introduced is a novel information theory-based predictability approach via relative entropy which captures extra information in higher order statistical moments of a forecast ensemble.