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.
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.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| Highlight paper
| 
14 Nov 2023
Review and perspective paper | Highlight paper |
| 14 Nov 2023
| 14 Nov 2023
Machine learning for numerical weather and climate modelling: a review
Catherine O. de Burgh-Day
CORRESPONDING AUTHOR
The Bureau of Meteorology, 700 Collins St, Docklands, Victoria, Australia
Tennessee Leeuwenburg
The Bureau of Meteorology, 700 Collins St, Docklands, Victoria, Australia
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Total article views: 6,029 (including HTML, PDF, and XML)
Thereof 5,986 with geography defined
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Total article views: 2,158 (including HTML, PDF, and XML)
Thereof 2,127 with geography defined
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Cited
19 citations as recorded by crossref.
- The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. 10.1002/qj.4858
- Climate-invariant machine learning T. Beucler et al. 10.1126/sciadv.adj7250
- Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast L. Olivetti & G. Messori 10.5194/gmd-17-7915-2024
- Embedding machine-learnt sub-grid variability improves climate model precipitation patterns D. Giles et al. 10.1038/s43247-024-01885-8
- Faster dieback of rainforests altering tropical carbon sinks under climate change D. Nath et al. 10.1038/s41612-024-00793-0
- Predictability of the anomaly pattern of summer extreme high temperature days over northern China J. Li & Y. Long 10.1007/s00382-024-07301-3
- A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research M. Kretschmer et al. 10.1002/asl.1268
- Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori 10.5194/gmd-17-2347-2024
- Tropical cyclone track prediction model for multidimensional features and time differences series observation P. Yang & G. Ye 10.1016/j.aej.2024.10.090
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific C. Liu et al. 10.1038/s41612-024-00769-0
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- Scientometric review on multiple climate-related hazards indices E. Laino et al. 10.1016/j.scitotenv.2024.174004
- Beyond coastal hazards: A comprehensive methodology for the assessment of climate-related hazards in European coastal cities E. Laino & G. Iglesias 10.1016/j.ocecoaman.2024.107343
- Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b Y. Zhang et al. 10.1038/s41598-024-73741-w
- Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation M. Shams Eddin & J. Gall 10.5194/gmd-17-2987-2024
- Application of machine learning to model the pressure poisson equation for fluid flow on generic geometries P. Sousa et al. 10.1007/s00521-024-09935-0
- Leveraging Artificial Intelligence to Address Climate Change T. Kumar et al. 10.38124/ijisrt/IJISRT24AUG020
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. 10.3390/jmse11091729
16 citations as recorded by crossref.
- The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. 10.1002/qj.4858
- Climate-invariant machine learning T. Beucler et al. 10.1126/sciadv.adj7250
- Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast L. Olivetti & G. Messori 10.5194/gmd-17-7915-2024
- Embedding machine-learnt sub-grid variability improves climate model precipitation patterns D. Giles et al. 10.1038/s43247-024-01885-8
- Faster dieback of rainforests altering tropical carbon sinks under climate change D. Nath et al. 10.1038/s41612-024-00793-0
- Predictability of the anomaly pattern of summer extreme high temperature days over northern China J. Li & Y. Long 10.1007/s00382-024-07301-3
- A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research M. Kretschmer et al. 10.1002/asl.1268
- Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori 10.5194/gmd-17-2347-2024
- Tropical cyclone track prediction model for multidimensional features and time differences series observation P. Yang & G. Ye 10.1016/j.aej.2024.10.090
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific C. Liu et al. 10.1038/s41612-024-00769-0
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
- Scientometric review on multiple climate-related hazards indices E. Laino et al. 10.1016/j.scitotenv.2024.174004
- Beyond coastal hazards: A comprehensive methodology for the assessment of climate-related hazards in European coastal cities E. Laino & G. Iglesias 10.1016/j.ocecoaman.2024.107343
- Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b Y. Zhang et al. 10.1038/s41598-024-73741-w
- Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation M. Shams Eddin & J. Gall 10.5194/gmd-17-2987-2024
3 citations as recorded by crossref.
- Application of machine learning to model the pressure poisson equation for fluid flow on generic geometries P. Sousa et al. 10.1007/s00521-024-09935-0
- Leveraging Artificial Intelligence to Address Climate Change T. Kumar et al. 10.38124/ijisrt/IJISRT24AUG020
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. 10.3390/jmse11091729
Latest update: 13 Dec 2024
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.
Machine Learning is a rapidly expanding technique in the field of weather and climate modelling....
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.
Machine learning (ML) is an increasingly popular tool in the field of weather and climate...
