Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2797-2019
https://doi.org/10.5194/gmd-12-2797-2019
Development and technical paper
 | 
10 Jul 2019
Development and technical paper |  | 10 Jul 2019

Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

Sebastian Scher and Gabriele Messori

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (15 May 2019)  Author's response
ED: Publish as is (29 May 2019) by Samuel Remy
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Short summary
Currently, weather forecasts are mainly produced by using computer models based on physical equations. It is an appealing idea to use neural networks and “deep learning” for weather forecasting instead. We successfully test the possibility of using deep learning for weather forecasting by considering climate models as simplified versions of reality. Our work therefore is a step towards potentially using deep learning to replace or accompany current weather forecasting models.