Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7215-2025
https://doi.org/10.5194/gmd-18-7215-2025
Methods for assessment of models
 | 
15 Oct 2025
Methods for assessment of models |  | 15 Oct 2025

Ensemble data assimilation to diagnose AI-based weather prediction models: a case with ClimaX version 0.3.1

Shunji Kotsuki, Kenta Shiraishi, and Atsushi Okazaki

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: 1,014 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
997 2 15 1,014 0 0
  • HTML: 997
  • PDF: 2
  • XML: 15
  • Total: 1,014
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 15 Oct 2024)
Cumulative views and downloads (calculated since 15 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: 1,014 (including HTML, PDF, and XML) Thereof 976 with geography defined and 38 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Oct 2025
Download
Short summary
Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored the coupling of ensemble data assimilation with an AI weather prediction model, ClimaX, which succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.
Share