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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Cited articles

Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Buschow, S. and Friederichs, P.: Local dimension and recurrent circulation patterns in long-term climate simulations, arXiv preprint arXiv:1803.11255, 2018. a
C3S: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 7 June 2019), 2017. a, b
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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.
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