On Some Limitations of Current Machine Learning Weather Prediction Models
Abstract Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We ex...
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| Format: | Article |
| Language: | English |
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Wiley
2024-06-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2023GL107377 |
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| _version_ | 1850271810289926144 |
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| author | Massimo Bonavita |
| author_facet | Massimo Bonavita |
| author_sort | Massimo Bonavita |
| collection | DOAJ |
| description | Abstract Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models. |
| format | Article |
| id | doaj-art-6a3bd5d21d574d65a0bc0c3d9d10999d |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-6a3bd5d21d574d65a0bc0c3d9d10999d2025-08-20T01:52:06ZengWileyGeophysical Research Letters0094-82761944-80072024-06-015112n/an/a10.1029/2023GL107377On Some Limitations of Current Machine Learning Weather Prediction ModelsMassimo Bonavita0ECMWF Reading UKAbstract Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models.https://doi.org/10.1029/2023GL107377machine learningnumerical weather predictiondata‐driven forecast models |
| spellingShingle | Massimo Bonavita On Some Limitations of Current Machine Learning Weather Prediction Models Geophysical Research Letters machine learning numerical weather prediction data‐driven forecast models |
| title | On Some Limitations of Current Machine Learning Weather Prediction Models |
| title_full | On Some Limitations of Current Machine Learning Weather Prediction Models |
| title_fullStr | On Some Limitations of Current Machine Learning Weather Prediction Models |
| title_full_unstemmed | On Some Limitations of Current Machine Learning Weather Prediction Models |
| title_short | On Some Limitations of Current Machine Learning Weather Prediction Models |
| title_sort | on some limitations of current machine learning weather prediction models |
| topic | machine learning numerical weather prediction data‐driven forecast models |
| url | https://doi.org/10.1029/2023GL107377 |
| work_keys_str_mv | AT massimobonavita onsomelimitationsofcurrentmachinelearningweatherpredictionmodels |