The promising potential of vision language models for the generation of textual weather forecasts

Edward C. C. Steele, Dinesh Mane, Emilio Monti, Luis Orus, Rebecca Chantrill-Cheyette +12 more
12/3/2025
cs.LGcs.AIphysics.ao-ph

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Abstract

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.

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Cite this paper

@article{steele2025promising,
  title  = {The promising potential of vision language models for the generation of textual weather forecasts},
  author = {Edward C. C. Steele and Dinesh Mane and Emilio Monti and Luis Orus and Rebecca Chantrill-Cheyette and Matthew Couch and Kirstine I. Dale and Simon Eaton and Govindarajan Rangarajan and Amir Majlesi and Steven Ramsdale and Michael Sharpe and Craig Smith and Jonathan Smith and Rebecca Yates and Holly Ellis and Charles Ewen},
  year   = {2025},
  eprint = {2512.03623},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.03623v1}
}

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