Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 16
Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5351-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 16
Model description paper
 | Highlight paper
 | 
27 Aug 2025
Model description paper | Highlight paper |  | 27 Aug 2025

GPTCast: a weather language model for precipitation nowcasting

Gabriele Franch, Elena Tomasi, Rishabh Wanjari, Virginia Poli, Chiara Cardinali, Pier Paolo Alberoni, and Marco Cristoforetti

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 527 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
519 2 6 527 1 0
  • HTML: 519
  • PDF: 2
  • XML: 6
  • Total: 527
  • BibTeX: 1
  • EndNote: 0
Views and downloads (calculated since 10 Oct 2024)
Cumulative views and downloads (calculated since 10 Oct 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 527 (including HTML, PDF, and XML) Thereof 516 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Aug 2025
Download
Executive editor
The application of machine learning techniques to weather forecasting is an exceptionally promising area for this technology. This paper presents an LLM nowcasting tool which outperforms existing technology for short term precipitation forecasting. This is an exciting demonstrator of the possibilities of this novel approach.
Read more
Short summary
Our research introduces GPTCast, a novel method for very short term precipitation forecasting using radar data. By applying advanced machine learning techniques inspired by large language models, we developed a system that generates accurate and realistic weather predictions. We trained the model using 6 years of radar data from northern Italy, demonstrating its superior performance over leading ensemble extrapolation methods.
Read more
Share