Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 22
Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6433-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6433-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 22
Review and perspective paper
 | Highlight paper
 | 
14 Nov 2023
Review and perspective paper | Highlight paper |  | 14 Nov 2023

Machine learning for numerical weather and climate modelling: a review

Catherine O. de Burgh-Day and Tennessee Leeuwenburg

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Executive editor
Machine Learning is a rapidly expanding technique in the field of weather and climate modelling. This paper takes stock of the state of the field at the present time, and will be invaluable to participants across the field and beyond who wish to understand the impact of Machine Learning on the field, its limitations, and current scope.
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Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
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