Challenges and design choices for global weather and climate models based on machine learning
<p>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 in deep-learning techniques. The questi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-10-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/11/3999/2018/gmd-11-3999-2018.pdf |
| Summary: | <p>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 in deep-learning techniques. The
question is valid given the huge amount of data that are 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.</p><p>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.</p> |
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| ISSN: | 1991-959X 1991-9603 |