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

Kotsuki, Shunji; Shiraishi, Kenta; Okazaki, Atsushi

doi:https://doi.org/10.5194/gmd-18-7215-2025

Articles | Volume 18, issue 20

https://doi.org/10.5194/gmd-18-7215-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-7215-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 20

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

Data sets

Experimental data, source codes and scripts used in Kotsuki et al. (2024) submitted to GMD Kotsuki (2024a) https://doi.org/10.5281/zenodo.13884167

Model code and software

Original source code of the ClimaX version 0.3.1 used in Kotsuki et al. (2024) submitted to GMD Kotsuki (2024b) https://doi.org/10.5281/zenodo.14258099

Original source code of the LETKF used in Kotsuki et al. (2024) submitted to GMD Kotsuki (2024c) https://doi.org/10.5281/zenodo.14258014

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.