Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 6
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.
https://doi.org/10.5194/gmd-18-2051-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 6
Development and technical paper
 | 
01 Apr 2025
Development and technical paper |  | 01 Apr 2025

Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations

Elena Tomasi, Gabriele Franch, 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: 2,705 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,508 182 15 2,705 32 61
  • HTML: 2,508
  • PDF: 182
  • XML: 15
  • Total: 2,705
  • BibTeX: 32
  • EndNote: 61
Views and downloads (calculated since 18 Sep 2024)
Cumulative views and downloads (calculated since 18 Sep 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: 2,705 (including HTML, PDF, and XML) Thereof 2,672 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 15 Sep 2025
Download
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.
Read more
Share