Articles | Volume 8, issue 2
Research article
28 Jun 2017
Research article |  | 28 Jun 2017

An efficient training scheme for supermodels

Francine J. Schevenhoven and Frank M. Selten

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Cited articles

Branicki, M. and Majda, A. J.: An Information-Theoretic Framework for Improving Imperfect Dynamical Predictions Via Multi-Model Ensemble Forecasts, J. Nonlinear Sci., 25, 489–538,, 2015.
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Du, H. and Smith, L.A.: Multi-model cross-pollination in time, Physica D, submitted, 2017.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 1535 pp., 2013.
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Short summary
Weather and climate models have improved steadily over time, but the models remain imperfect. Given these imperfect models, predictions might be improved by combining the models into a so-called “supermodel”. In this paper we show a new method to construct such a supermodel. The results indicate that the supermodel has superior forecast quality compared to the individual models.
Final-revised paper