The promising potential of vision language models for the generation of textual weather forecasts
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915651200745472 |
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| author | Steele, Edward C. C. Mane, Dinesh Monti, Emilio Orus, Luis Chantrill-Cheyette, Rebecca Couch, Matthew Dale, Kirstine I. Eaton, Simon Rangarajan, Govindarajan Majlesi, Amir Ramsdale, Steven Sharpe, Michael Smith, Craig Smith, Jonathan Yates, Rebecca Ellis, Holly Ewen, Charles |
| author_facet | Steele, Edward C. C. Mane, Dinesh Monti, Emilio Orus, Luis Chantrill-Cheyette, Rebecca Couch, Matthew Dale, Kirstine I. Eaton, Simon Rangarajan, Govindarajan Majlesi, Amir Ramsdale, Steven Sharpe, Michael Smith, Craig Smith, Jonathan Yates, Rebecca Ellis, Holly Ewen, Charles |
| contents | Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The promising potential of vision language models for the generation of textual weather forecasts Steele, Edward C. C. Mane, Dinesh Monti, Emilio Orus, Luis Chantrill-Cheyette, Rebecca Couch, Matthew Dale, Kirstine I. Eaton, Simon Rangarajan, Govindarajan Majlesi, Amir Ramsdale, Steven Sharpe, Michael Smith, Craig Smith, Jonathan Yates, Rebecca Ellis, Holly Ewen, Charles Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond. |
| title | The promising potential of vision language models for the generation of textual weather forecasts |
| topic | Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2512.03623 |