Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models

Contemporary tools for reducing model error in weather and climate forecasting models include empirical correction techniques. In this paper we explore the use of such techniques on low-order atmospheric models. We first present an iterative linear regression method for model cor- rection that works efficiently when the reference truth is sam- pled at large time intervals, which is typical for real world ap- plications. Furthermore we investigate two recently proposed empirical correction techniques on Lorenz models with con- stant forcing while the reference truth is given by a Lorenz system driven with chaotic forcing. Both methods indicate that the largest increase in predictability comes from correc- tion terms that are close to the average value of the chaotic forcing.

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