Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8873-2024
https://doi.org/10.5194/gmd-17-8873-2024
Model experiment description paper
 | 
13 Dec 2024
Model experiment description paper |  | 13 Dec 2024

Architectural insights into and training methodology optimization of Pangu-Weather

Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus

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Cited articles

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/s41586-023-06185-3, 2023a. a, b, c, d, e, f, g, h, i, j, k
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Pangu-Weather: An official implementation of Pangu-Weather, GitHub repository [code], https://github.com/198808xc/Pangu-Weather/tree/main (last access: 6 June 2024), 2023b. a, b
Bodnar, C., Bruinsma, W., Lucic, A., Stanley, M., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J., Dong, H., Vaughan, A., Gupta, J., Tambiratnam, K., Archibald, A., Heider, E., Welling, M., Turner, R., and Perdikaris, P.: Aurora: A Foundation Model of the Atmosphere, Tech. Rep., MSR-TR-2024-16, Microsoft Research AI for Science, https://www.microsoft.com/en-us/research/publication/aurora-a-foundation-model-of-the-atmosphere/ (last access: 4 December 2024), 2024. a
Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., Ci, Y., Li, B., Yang, X., and Ouyang, W.: FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02948, 2023a. a
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., and Li, H.: FuXi: a cascade machine learning forecasting system for 15-day global weather forecast, npj Climate and Atmospheric Science, 6, 190, https://doi.org/10.1038/s41612-023-00512-1, 2023b. a
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Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
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