Articles | Volume 10, issue 4
Research article
28 Nov 2019
Research article |  | 28 Nov 2019

Improving weather and climate predictions by training of supermodels

Francine Schevenhoven, Frank Selten, Alberto Carrassi, and Noel Keenlyside

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Cited articles

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Short summary
Weather and climate predictions potentially improve by dynamically combining different models into a supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
Final-revised paper