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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-148
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 28 Jun 2018

Development and technical paper | 28 Jun 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

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

Peter D. Dueben and Peter Bauer Peter D. Dueben and Peter Bauer
  • European Centre for Medium-range Weather Forecasts, Shinfield Rd, Reading RG2 9AX

Abstract. Can models that are based on deep learning and trained on atmospheric data compete with weather and climate models that are based on physical principles and the basic equations of motion? This question has been asked often recently due to the boom of deep learning techniques. The question is valid given the huge amount of data that is available, the computational efficiency of deep learning techniques and the limitations of today's weather and climate models in particular with respect to resolution and complexity.

In this paper, the question will be discussed 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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Peter D. Dueben and Peter Bauer
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Peter D. Dueben and Peter Bauer
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Short summary
We discuss the question 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.
We discuss the question whether weather forecast models that are based on deep learning and...
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