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

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Cited articles

Adrian, M., Sanz-Alonso, D., and Willett, R.: Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet, Artif. Intell. Earth Syst., 4, e240050, https://doi.org/10.1175/AIES-D-24-0050.1, 2025. 
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06545-z, 2023. 
Bocquet, M., Farchi, A., Finn, T. S., Durand, C., Cheng, S., Chen, Y., Pasmans, I., and Carrassi, A.: Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble, Chaos, 34, 091104, https://doi.org/10.1063/5.0230837, 2024. 
Bonavita, M.: On some limitations of current machine learning weather prediction models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024. 
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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.
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