Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7915-2024
https://doi.org/10.5194/gmd-17-7915-2024
Model evaluation paper
 | 
07 Nov 2024
Model evaluation paper |  | 07 Nov 2024

Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

Leonardo Olivetti and Gabriele Messori

Related authors

Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary

Related subject area

Atmospheric sciences
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024,https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024,https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024,https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024,https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary

Cited articles

Abrahams, A., Schlegel, R. W., and Smit, A. J.: Variation and Change of Upwelling Dynamics Detected in the World’s Eastern Boundary Upwelling Systems, Frontiers in Marine Science, 8, https://doi.org/10.3389/fmars.2021.626411, 2021. a
Arellano, M.: PRACTITIONERS’ CORNER: Computing Robust Standard Errors for Within-groups Estimators, Oxford B. Econ. Stat., 49, 431–434, https://doi.org/10.1111/j.1468-0084.1987.mp49004006.x, 1987. a
Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, J. R. Stat. Soc. B, 57, 289–300, https://www.jstor.org/stable/2346101 (last access: 28 October 2024​​​​​​​), 1995. a
Beucler, T., Pritchard, M., Gentine, P., and Rasp, S.: Towards Physically-Consistent, Data-Driven Models of Convection, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 26 September–2 October 2020, Waikoloa, HI, USA, online, 3987–3990, https://doi.org/10.1109/IGARSS39084.2020.9324569, 2020. a
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast, arXiv [preprint], https://doi.org/10.48550/arXiv.2211.02556, 2022. a
Download
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.