Blending machine learning and physics-based approaches for weather and climate: a typology
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918513774428160 |
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| author | Shipway, Benjamin J Bain, Caroline Walters, David Booth, Ben B. B. Boutle, Ian Clark, Robin T. Hill, Katherine L. Kendon, Elizabeth Vosper, Simon B. |
| author_facet | Shipway, Benjamin J Bain, Caroline Walters, David Booth, Ben B. B. Boutle, Ian Clark, Robin T. Hill, Katherine L. Kendon, Elizabeth Vosper, Simon B. |
| contents | The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we "blend" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approaches and discusses some of the strategic benefits that come with them. It can be used not just to classify modelling systems, but also identify routes to gradual, incremental or wholesale development and implementation of new and emerging capabilities. These approaches provide a practical path to innovation by combining the speed and adaptability of machine learning with the robustness, trust, and interpretability of physics-based systems. By adopting a structured vocabulary and outlining the benefits and limitations of each approach, this framework supports informed decision-making and strategic planning, and can be used by the wider community to navigate the transition to next-generation prediction systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20925 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Blending machine learning and physics-based approaches for weather and climate: a typology Shipway, Benjamin J Bain, Caroline Walters, David Booth, Ben B. B. Boutle, Ian Clark, Robin T. Hill, Katherine L. Kendon, Elizabeth Vosper, Simon B. Atmospheric and Oceanic Physics The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we "blend" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approaches and discusses some of the strategic benefits that come with them. It can be used not just to classify modelling systems, but also identify routes to gradual, incremental or wholesale development and implementation of new and emerging capabilities. These approaches provide a practical path to innovation by combining the speed and adaptability of machine learning with the robustness, trust, and interpretability of physics-based systems. By adopting a structured vocabulary and outlining the benefits and limitations of each approach, this framework supports informed decision-making and strategic planning, and can be used by the wider community to navigate the transition to next-generation prediction systems. |
| title | Blending machine learning and physics-based approaches for weather and climate: a typology |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2605.20925 |