Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models
- 1Macedonian Academy of Sciences and Arts, Skopje, Macedonia
- 2SS. Cyril and Methodius University, Faculty of Computer Science and Engineering, P.O. Box 393, Skopje, Macedonia
- 3The MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
- 4BioCircuits Institute, University of California, San Diego, California, USA
Abstract. 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 correction that works efficiently when the reference truth is sampled at large time intervals, which is typical for real world applications. Furthermore we investigate two recently proposed empirical correction techniques on Lorenz models with constant 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 correction terms that are close to the average value of the chaotic forcing.