Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
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

Related authors

Bottom–up approach for mitigating extreme events with limited intervention options: a case study with Lorenz 96 model
Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda
Nonlin. Processes Geophys., 32, 457–469, https://doi.org/10.5194/npg-32-457-2025,https://doi.org/10.5194/npg-32-457-2025, 2025
Short summary
Observation error estimation in climate proxies with data assimilation and innovation statistics
Atsushi Okazaki, Diego S. Carrió, Quentin Dalaiden, Jarrah Harrison-Lofthouse, Shunji Kotsuki, and Kei Yoshimura
Clim. Past, 21, 1801–1819, https://doi.org/10.5194/cp-21-1801-2025,https://doi.org/10.5194/cp-21-1801-2025, 2025
Short summary
Numerical Experiments of Cloud Seeding for Mitigating Localization of Heavy Rainfall: A Case Study of Mesoscale Convective System in Japan
Yusuke Hiraga, Jacqueline Muthoni Mbugua, Shunji Kotsuki, Yoshiharu Suzuki, Shu-Hua Chen, Atsushi Hamada, Kazuaki Yasunaga, and Takuya Funatomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3524,https://doi.org/10.5194/egusphere-2025-3524, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Ensemble-based model predictive control using data assimilation techniques
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
Nonlin. Processes Geophys., 32, 293–307, https://doi.org/10.5194/npg-32-293-2025,https://doi.org/10.5194/npg-32-293-2025, 2025
Short summary
Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control
Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya
Nonlin. Processes Geophys., 32, 281–292, https://doi.org/10.5194/npg-32-281-2025,https://doi.org/10.5194/npg-32-281-2025, 2025
Short summary
More articles (9)

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. 
Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Allen, A., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J. A., Dong, H., Gupta, J. K., Thambiratnam, K., Archibald, A. T., Wu, C.-C., Heider, E., Welling, M., Turner, R. E., and Perdikaris, P.: A foundation model for the Earth System, Nature, 641, 1180–1187, https://doi.org/10.1038/s41586-025-09005-y, 2025. 
Bonavita, M.: On some limitations of current machine learning weather prediction models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024. 
More articles (40)
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.
Read more
Share