Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2347-2024
© 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-17-2347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| Highlight paper
| 
21 Mar 2024
Review and perspective paper | Highlight paper |
| 21 Mar 2024
| 21 Mar 2024
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, Uppsala University, 75236 Uppsala, Sweden
Gabriele Messori
Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, Uppsala University, 75236 Uppsala, Sweden
Department of Meteorology, Stockholm University, 10691 Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
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Cited
19 citations as recorded by crossref.
- Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. https://doi.org/10.1038/s41467-025-56573-8
- HybridFlow: A Hybrid Velocity Generation Framework for Precipitation Nowcasting X. Ling et al. https://doi.org/10.1109/TGRS.2025.3646177
- Physics-Informed spatiotemporal deep learning for multivariate atmospheric forecasting H. Yu et al. https://doi.org/10.1016/j.eswa.2026.131721
- An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al. https://doi.org/10.5194/gmd-18-5781-2025
- Spatiotemporal Adaptive Correction for WRF Surface Parameters: A Fusion Approach of GNSS ZTD and Deep Attention Networks P. Wei et al. https://doi.org/10.1109/LGRS.2025.3626740
- FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction X. Zhong et al. https://doi.org/10.1126/sciadv.adu2854
- Skillful heat-related mortality forecasting during recent deadly European summers E. Holmberg et al. https://doi.org/10.1073/pnas.2426516122
- Predictability assessment of cold-wet-windy pan-Atlantic extremes M. Krouma & G. Messori https://doi.org/10.1016/j.wace.2026.100903
- Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue https://doi.org/10.3390/atmos15060689
- Can data-driven weather models accurately forecast atmospheric rivers? F. Lopez-Marti et al. https://doi.org/10.1088/1748-9326/ae1e8e
- Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast L. Olivetti & G. Messori https://doi.org/10.5194/gmd-17-7915-2024
- Inverse differential equation modeling of ENSO prediction based on memory kernel functions S. Wan et al. https://doi.org/10.1007/s00382-025-07719-3
- Numerical prediction of solar radiation in South Korea: assessment of WRF-Solar and WRF J. Yoon et al. https://doi.org/10.1016/j.ecmx.2025.101253
- Improving localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection M. Zanchi et al. https://doi.org/10.1016/j.envsoft.2025.106509
- A Hybrid Approach for Seasonal Rainfall Forecasting Across Vietnam Using Convolutional Neural Networks and Dynamical Downscaling T. Ngoc et al. https://doi.org/10.1007/s00024-025-03838-4
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Machine Learning Methods for Weather Forecasting: A Survey H. Zhang et al. https://doi.org/10.3390/atmos16010082
- Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study B. Kuhaneswaran et al. https://doi.org/10.3390/w17152281
- Optimization of Parallel Fourier Transform in YHGSM Based on Computation–Communication Overlap Y. Zheng et al. https://doi.org/10.3390/electronics14163238
19 citations as recorded by crossref.
- Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. https://doi.org/10.1038/s41467-025-56573-8
- HybridFlow: A Hybrid Velocity Generation Framework for Precipitation Nowcasting X. Ling et al. https://doi.org/10.1109/TGRS.2025.3646177
- Physics-Informed spatiotemporal deep learning for multivariate atmospheric forecasting H. Yu et al. https://doi.org/10.1016/j.eswa.2026.131721
- An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al. https://doi.org/10.5194/gmd-18-5781-2025
- Spatiotemporal Adaptive Correction for WRF Surface Parameters: A Fusion Approach of GNSS ZTD and Deep Attention Networks P. Wei et al. https://doi.org/10.1109/LGRS.2025.3626740
- FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction X. Zhong et al. https://doi.org/10.1126/sciadv.adu2854
- Skillful heat-related mortality forecasting during recent deadly European summers E. Holmberg et al. https://doi.org/10.1073/pnas.2426516122
- Predictability assessment of cold-wet-windy pan-Atlantic extremes M. Krouma & G. Messori https://doi.org/10.1016/j.wace.2026.100903
- Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue https://doi.org/10.3390/atmos15060689
- Can data-driven weather models accurately forecast atmospheric rivers? F. Lopez-Marti et al. https://doi.org/10.1088/1748-9326/ae1e8e
- Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast L. Olivetti & G. Messori https://doi.org/10.5194/gmd-17-7915-2024
- Inverse differential equation modeling of ENSO prediction based on memory kernel functions S. Wan et al. https://doi.org/10.1007/s00382-025-07719-3
- Numerical prediction of solar radiation in South Korea: assessment of WRF-Solar and WRF J. Yoon et al. https://doi.org/10.1016/j.ecmx.2025.101253
- Improving localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection M. Zanchi et al. https://doi.org/10.1016/j.envsoft.2025.106509
- A Hybrid Approach for Seasonal Rainfall Forecasting Across Vietnam Using Convolutional Neural Networks and Dynamical Downscaling T. Ngoc et al. https://doi.org/10.1007/s00024-025-03838-4
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Machine Learning Methods for Weather Forecasting: A Survey H. Zhang et al. https://doi.org/10.3390/atmos16010082
- Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study B. Kuhaneswaran et al. https://doi.org/10.3390/w17152281
- Optimization of Parallel Fourier Transform in YHGSM Based on Computation–Communication Overlap Y. Zheng et al. https://doi.org/10.3390/electronics14163238
Saved (final revised paper)
Latest update: 13 Jun 2026
Editorial statement
This article provides a concise and well-written review of the current state of numerical weather prediction using machine learning models. Given how quickly this field is evolving, it's difficult for the traditional peer review process to capture all developments in this space, but this manuscript provides an excellent snapshot of the current state of the art.
This article provides a concise and well-written review of the current state of numerical...
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
In the last decades, weather forecasting up to 15 d into the future has been dominated by...
