Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7915-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7915-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
07 Nov 2024
Model evaluation paper |
| 07 Nov 2024
| 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
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden
Swedish Centre for Impacts of Climate Extremes (climes), Uppsala University, 75236 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala University, 75236 Uppsala, Sweden
Gabriele Messori
Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden
Swedish Centre for Impacts of Climate Extremes (climes), Uppsala University, 75236 Uppsala, Sweden
Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
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Cited
20 citations as recorded by crossref.
- Binned spectral power loss for improved prediction of chaotic systems D. Chakraborty et al. https://doi.org/10.1016/j.jcp.2026.114866
- Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western US J. Ryu et al. https://doi.org/10.5194/gmd-19-27-2026
- Resolving the limits of MJO forecast skill: large-sample-based ensemble optimization in the IAP-CAS S2S model Y. Liu et al. https://doi.org/10.3389/fclim.2026.1828808
- Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models: A Case Study for Typhoon Danas (2025) Z. Niu et al. https://doi.org/10.1007/s00376-025-5464-8
- Can data-driven weather models accurately forecast atmospheric rivers? F. Lopez-Marti et al. https://doi.org/10.1088/1748-9326/ae1e8e
- Data-driven forecasts of extreme weather in East Asia: feasibility of operational use S. Oh et al. https://doi.org/10.1016/j.wace.2026.100875
- Ensemble Experiments in an AI-NWP Coupled Framework: A Typhoon Case Y. Ge et al. https://doi.org/10.1007/s13351-025-5038-9
- Evaluating deep learning-integrated physics-based models for tropical cyclone track and intensity predictions Y. Zeng et al. https://doi.org/10.1063/5.0303579
- Foundation Models of Ocean and Atmosphere in 2025: Milestones and Perspectives M. Krinitskiy https://doi.org/10.3103/S0027134925703084
- Fusion of multi-source precipitation records via coordinate-based generative models S. Sun et al. https://doi.org/10.1038/s41467-025-67987-9
- Machine learning advances and data model coevolution in geoscience A. Eltijnai & M. Mohammed https://doi.org/10.1007/s44288-026-00524-3
- Explaining AI’s successes: A no miracles argument for quasi-representations D. Rowbottom & A. Curtis-Trudel https://doi.org/10.1007/s11229-026-05525-w
- The potential of AI global weather models for reference evapotranspiration forecasting: a comparison with numerical weather prediction models S. Zhao et al. https://doi.org/10.1016/j.jhydrol.2025.134363
- Toward Operational Heat-Stress Early Warnings in Southern South America: AI-Based UTCI Forecasting S. Collazo et al. https://doi.org/10.1007/s41748-026-01194-4
- Impact of AIFS and GFS Initialization on WRF Operational Forecasts During High-Impact Storms in Spain (2025) R. Arasa Agudo et al. https://doi.org/10.3390/earth7030077
- Physics-based models outperform AI weather forecasts of record-breaking extremes Z. Zhang et al. https://doi.org/10.1126/sciadv.aec1433
- An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al. https://doi.org/10.5194/gmd-18-5781-2025
- MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones C. Dauvilliers & C. Monteleoni https://doi.org/10.1017/eds.2025.10014
- Data-Driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models Y. Hirabayashi & D. Matsuoka https://doi.org/10.2151/sola.2025-062
- Whose weather is it? A fairness framework for data-driven weather forecasting L. Olivetti & G. Messori https://doi.org/10.1088/1748-9326/ae21f5
20 citations as recorded by crossref.
- Binned spectral power loss for improved prediction of chaotic systems D. Chakraborty et al. https://doi.org/10.1016/j.jcp.2026.114866
- Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western US J. Ryu et al. https://doi.org/10.5194/gmd-19-27-2026
- Resolving the limits of MJO forecast skill: large-sample-based ensemble optimization in the IAP-CAS S2S model Y. Liu et al. https://doi.org/10.3389/fclim.2026.1828808
- Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models: A Case Study for Typhoon Danas (2025) Z. Niu et al. https://doi.org/10.1007/s00376-025-5464-8
- Can data-driven weather models accurately forecast atmospheric rivers? F. Lopez-Marti et al. https://doi.org/10.1088/1748-9326/ae1e8e
- Data-driven forecasts of extreme weather in East Asia: feasibility of operational use S. Oh et al. https://doi.org/10.1016/j.wace.2026.100875
- Ensemble Experiments in an AI-NWP Coupled Framework: A Typhoon Case Y. Ge et al. https://doi.org/10.1007/s13351-025-5038-9
- Evaluating deep learning-integrated physics-based models for tropical cyclone track and intensity predictions Y. Zeng et al. https://doi.org/10.1063/5.0303579
- Foundation Models of Ocean and Atmosphere in 2025: Milestones and Perspectives M. Krinitskiy https://doi.org/10.3103/S0027134925703084
- Fusion of multi-source precipitation records via coordinate-based generative models S. Sun et al. https://doi.org/10.1038/s41467-025-67987-9
- Machine learning advances and data model coevolution in geoscience A. Eltijnai & M. Mohammed https://doi.org/10.1007/s44288-026-00524-3
- Explaining AI’s successes: A no miracles argument for quasi-representations D. Rowbottom & A. Curtis-Trudel https://doi.org/10.1007/s11229-026-05525-w
- The potential of AI global weather models for reference evapotranspiration forecasting: a comparison with numerical weather prediction models S. Zhao et al. https://doi.org/10.1016/j.jhydrol.2025.134363
- Toward Operational Heat-Stress Early Warnings in Southern South America: AI-Based UTCI Forecasting S. Collazo et al. https://doi.org/10.1007/s41748-026-01194-4
- Impact of AIFS and GFS Initialization on WRF Operational Forecasts During High-Impact Storms in Spain (2025) R. Arasa Agudo et al. https://doi.org/10.3390/earth7030077
- Physics-based models outperform AI weather forecasts of record-breaking extremes Z. Zhang et al. https://doi.org/10.1126/sciadv.aec1433
- An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al. https://doi.org/10.5194/gmd-18-5781-2025
- MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones C. Dauvilliers & C. Monteleoni https://doi.org/10.1017/eds.2025.10014
- Data-Driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models Y. Hirabayashi & D. Matsuoka https://doi.org/10.2151/sola.2025-062
- Whose weather is it? A fairness framework for data-driven weather forecasting L. Olivetti & G. Messori https://doi.org/10.1088/1748-9326/ae21f5
Saved (final revised paper)
Latest update: 03 Jun 2026
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.
Data-driven models are becoming a viable alternative to physics-based models for weather...
