Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 11, issue 10
Articles | Volume 11, issue 10
https://doi.org/10.5194/gmd-11-3999-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-3999-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 10
Development and technical paper
 | 
01 Oct 2018
Development and technical paper |  | 01 Oct 2018

Challenges and design choices for global weather and climate models based on machine learning

Peter D. Dueben and Peter Bauer

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Short summary
We discuss the question of whether weather forecast models that are based on deep learning and trained on atmospheric data can compete with conventional weather and climate models that are based on physical principles and the basic equations of motion. We discuss the question in the context of global weather forecasts. A toy model for global weather predictions will be presented and used to identify challenges and fundamental design choices for a forecast system based on neural networks.
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