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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.17658 |
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| _version_ | 1866914072324210688 |
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| author | Daub, Eric G. Dunstan, Tom Bennett, Thusal Burnand, Matthew Chappell, James Coca-Castro, Alejandro Eftekhari, Noushin Hosking, J. Scott Janmaijaya, Manvendra Lillis, Jon Salvador-Jasin, David Simpson, Nathan Strickson, Oliver T Chan, Ryan Sze-Yin Elmasri, Mohamad France, Lydia Allegranza Madge, Sam Owen, Aled Robinson, James Scaife, Adam A. Walters, David Yatsyshin, Peter McCaie, Theo Bokeria, Levan Brown, Hannah Dodds, Tom Llewellyn-Jones, David Moreton, Sophia Potter, Tom Stenson, Iain van Zeeland, Louisa Bett-Williams, Karina Dale, Kirstine Ida |
| author_facet | Daub, Eric G. Dunstan, Tom Bennett, Thusal Burnand, Matthew Chappell, James Coca-Castro, Alejandro Eftekhari, Noushin Hosking, J. Scott Janmaijaya, Manvendra Lillis, Jon Salvador-Jasin, David Simpson, Nathan Strickson, Oliver T Chan, Ryan Sze-Yin Elmasri, Mohamad France, Lydia Allegranza Madge, Sam Owen, Aled Robinson, James Scaife, Adam A. Walters, David Yatsyshin, Peter McCaie, Theo Bokeria, Levan Brown, Hannah Dodds, Tom Llewellyn-Jones, David Moreton, Sophia Potter, Tom Stenson, Iain van Zeeland, Louisa Bett-Williams, Karina Dale, Kirstine Ida |
| contents | We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan Turing Institute and the Met Office. FastNet uses an encode-process-decode structure to produce deterministic global weather predictions out to 10 days. The architecture is independent of spatial resolution and we have trained models at 1$^{\circ}$ and 0.25$^{\circ}$ resolution, with a six hour time step. FastNet uses a multi-level mesh in the processor, which is able to capture both short-range and long-range patterns in the spatial structure of the atmosphere. The model is pre-trained on ECMWF's ERA5 reanalysis data and then fine-tuned on additional autoregressive rollout steps, which improves accuracy over longer time horizons. We evaluate the model performance at 1.5$^{\circ}$ resolution using 2022 as a hold-out year and compare with the Met Office Global Model, finding that FastNet surpasses the skill of the current Met Office Global Model NWP system using a variety of evaluation metrics on a number of atmospheric variables. Our results show that both our 1$^{\circ}$ and 0.25$^{\circ}$ FastNet models outperform the current Global Model and produce results with predictive skill approaching those of other data-driven models trained on 0.25$^{\circ}$ ERA5. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0 Daub, Eric G. Dunstan, Tom Bennett, Thusal Burnand, Matthew Chappell, James Coca-Castro, Alejandro Eftekhari, Noushin Hosking, J. Scott Janmaijaya, Manvendra Lillis, Jon Salvador-Jasin, David Simpson, Nathan Strickson, Oliver T Chan, Ryan Sze-Yin Elmasri, Mohamad France, Lydia Allegranza Madge, Sam Owen, Aled Robinson, James Scaife, Adam A. Walters, David Yatsyshin, Peter McCaie, Theo Bokeria, Levan Brown, Hannah Dodds, Tom Llewellyn-Jones, David Moreton, Sophia Potter, Tom Stenson, Iain van Zeeland, Louisa Bett-Williams, Karina Dale, Kirstine Ida Atmospheric and Oceanic Physics We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan Turing Institute and the Met Office. FastNet uses an encode-process-decode structure to produce deterministic global weather predictions out to 10 days. The architecture is independent of spatial resolution and we have trained models at 1$^{\circ}$ and 0.25$^{\circ}$ resolution, with a six hour time step. FastNet uses a multi-level mesh in the processor, which is able to capture both short-range and long-range patterns in the spatial structure of the atmosphere. The model is pre-trained on ECMWF's ERA5 reanalysis data and then fine-tuned on additional autoregressive rollout steps, which improves accuracy over longer time horizons. We evaluate the model performance at 1.5$^{\circ}$ resolution using 2022 as a hold-out year and compare with the Met Office Global Model, finding that FastNet surpasses the skill of the current Met Office Global Model NWP system using a variety of evaluation metrics on a number of atmospheric variables. Our results show that both our 1$^{\circ}$ and 0.25$^{\circ}$ FastNet models outperform the current Global Model and produce results with predictive skill approaching those of other data-driven models trained on 0.25$^{\circ}$ ERA5. |
| title | Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0 |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2509.17658 |