Preprints
https://doi.org/10.5194/egusphere-2023-350
https://doi.org/10.5194/egusphere-2023-350
17 Apr 2023
 | 17 Apr 2023
Status: this preprint is open for discussion.

Machine Learning for numerical weather and climate modelling: a review

Catherine Odelia de Burgh-Day and Tennessee Leeuwenburg

Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parametrisation scheme emulation and replacement, and recently even to full ML-based weather and climate prediction models. While ML has been used in this space for more than 25 years, it is only in the last 10 or so years that progress has accelerated to the point that ML applications are becoming competitive with numerical knowledge-based alternatives. In this review, we provide a roughly chronological summary of the application of ML to aspects of weather and climate modelling from early publications through to the latest progress at the time of writing. We also provide an overview of key ML concepts and terms. Our aim is to provide a primer for researchers and model developers to rapidly familiarize and update themselves with the world of ML in the context of weather and climate models.

Catherine Odelia de Burgh-Day and Tennessee Leeuwenburg

Status: open (until 12 Jun 2023)

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  • RC1: 'Comment on egusphere-2023-350', Anonymous Referee #1, 17 May 2023 reply

Catherine Odelia de Burgh-Day and Tennessee Leeuwenburg

Catherine Odelia de Burgh-Day and Tennessee Leeuwenburg

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Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. It has been used to improve many components of these models, and even the entire model. 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 approaches. In this review, we have summarized the use of ML in weather and climate modelling over time, and have also provided an overview of key ML concepts and terms.