Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2051-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2051-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Apr 2025
Development and technical paper |
| 01 Apr 2025
| 01 Apr 2025
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
Gabriele Franch
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
Marco Cristoforetti
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
Related authors
Gabriele Franch, Elena Tomasi, Rishabh Wanjari, Virginia Poli, Chiara Cardinali, Pier Paolo Alberoni, and Marco Cristoforetti
Geosci. Model Dev., 18, 5351–5371, https://doi.org/10.5194/gmd-18-5351-2025, https://doi.org/10.5194/gmd-18-5351-2025, 2025
Short summary
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.
Gabriele Franch, Elena Tomasi, Rishabh Wanjari, Virginia Poli, Chiara Cardinali, Pier Paolo Alberoni, and Marco Cristoforetti
Geosci. Model Dev., 18, 5351–5371, https://doi.org/10.5194/gmd-18-5351-2025, https://doi.org/10.5194/gmd-18-5351-2025, 2025
Short summary
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.
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Short summary
High-resolution weather data are crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using latent diffusion models (LDMs) to mimic this process, increasing weather data resolution over Italy from 25 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
High-resolution weather data are crucial for many applications, typically generated via...
