Cited by 155Meteorological Phenomena and SimulationsHydrological Forecasting Using AIModel Reduction and Neural Networks

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

On Some Limitations of Current Machine Learning Weather Prediction Models

Open access

Bonavita, Massimo·2024·Geophysical Research Letters

DOI 10.1029/2023gl107377

Open this paper in CoChat — free account

Abstract

Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models.

Integrity signals

Registry

Crossref · 10.1029/2023gl107377

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The ERA5 global reanalysis
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3803 · Cited by 31,882
Self-citation
An Introduction to Dynamic Meteorology
Holton, J. R., & Staley, D. O. (1973) · American Journal of Physics · doi:10.1119/1.1987371 · Cited by 3,425
Learning skillful medium-range global weather forecasting
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., et al. (2023) · Science · doi:10.1126/science.adi2336 · Cited by 1,466
MetNet: A Neural Weather Model for Precipitation ForecastingPreprint
Sønderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J. J., & Kalchbrenner, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12140 · Cited by 507
Complete ERA5 global atmospheric reanalysis
Service, C. C. C. (2023) · European Centre for Medium-Range Weather Forecasts · doi:10.24381/cds.143582cf · Cited by 238
The evolution of the ECMWF hybrid data assimilation system
Bonavita, M., Hólm, E. V., Isaksen, L., & Fisher, M. (2015) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2652 · Cited by 222
Self-citation
The Ceaseless Wind: An Introduction to the Theory of Atmospheric Motion
Dutton, J. A. (1976) · Cited by 197
Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect?
Selz, T., & Craig, G. C. (2023) · Geophysical Research Letters · doi:10.1029/2023gl105747 · Cited by 109
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting
Chen, L., Fei, D., Hu, Y., Wang, Z., & Wang, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25105 · Cited by 72
Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation
Pu, Z., & Kalnay, E. (2018) · Springer eBooks · doi:10.1007/978-3-642-40457-3_11-1 · Cited by 55

Show all 13 references

Research works printed but not linked: 17

Ben‐Bouallegue Z. Clare M. C. Magnusson L. Gascon E. Maier‐Gerber M. Janoušek M. et al. (2023).The rise of data‐driven weather forecasting. arXiv preprint arXiv:2307.1012.
Bi K. Xie L. Zhang H. Chen X. Gu X. &Tian Q.(2022).Pangu‐weather: A 3D high‐resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556. · arXiv
10.1038/s41586‐023‐06185‐3 · DOI · Paper page
10.1038/s41612‐023‐00387‐2 · DOI
Chen K. Han T. Gong J. Bai L. Ling F. Luo J.‐J. et al. (2023).FengWu: Pushing the skillful global medium‐range weather forecast beyond 10 days lead. arXiv preprint arXiv:2304.02948. · arXiv
Dosovitskyi A. Beyer L. Kolesnikov A. Weissenborn D. Zhai X. Unterthiner T. et al. (2021).An image is worth 16×16 words: Transformers for image recognition at scale. arXiv:2010.11929. · arXiv
Keisler R.(2022).Forecasting global weather with graph neural networks. arXiv preprint arXiv:2202.07575. · arXiv
Knapp K. R., 2018, International best track archive for climate stewardship (IBTrACS) project, version 4
Krasnopolsky V., 2023, Review: Using machine learning for data assimilation, model physics, and post‐processing model outputs, 513
10.1175/1520‐0493(1974)102<0409:tsomcf>2.0.co;2 · DOI
Pathak J. Subramanian S. Harrington P. Raja S. Chattopadhyay A. Mardani M. et al. (2022).Fourcastnet: A global data‐driven high‐resolution weather model using adaptive Fourier neural operators. arXiv preprint arXiv:2202.11214. · arXiv
Sanchez‐Gonzalez A., 2020, International Conference on Machine Learning, 8459
10.1038/s41612‐022‐00269‐z · DOI
10.22541/essoar.169111361.10601606/v1 · DOI
10.1175/15200493(1997)125<3297:EFANAT>2.0.CO;2 · DOI
Tung N. Brandstetter J. Kapoor A. Gupta J. K. &Grover A.(2023).ClimaX: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343. · arXiv
Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A. N. et al. (2017).Attention is all you need. arXiv:1706.03762. · arXiv

Sorted by citations

Neural general circulation models for weather and climate
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454
“A recent study 16 highlighted that Pangu misrepresents the vertical structure of the geostrophic and ageostrophic winds and noted a deterioration at longer lead times.”
Background
Machine learning for the physics of climate
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00776-3 · Cited by 79
Forecasting the eddying ocean with a deep neural network
Cui, Y., Wu, R., Zhang, X., Zhu, Z., Liu, B., Shi, J., Chen, J., Liu, H., Zhou, S., Liang, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57389-2 · Cited by 84
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Sun, Y. Q., Hassanzadeh, P., Zand, M., Chattopadhyay, A., Weare, J., & Abbot, D. S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420914122 · Cited by 83
Modeling the complex spatio-temporal dynamics of ocean wave parameters: A hybrid PINN-LSTM approach for accurate wave forecasting
Lawal, Z. K., Yassin, H., Lai, D. T. C., & Idris, A. C. (2025) · Measurement · doi:10.1016/j.measurement.2025.117383 · Cited by 48
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Olivetti, L., & Messori, G. (2024) · Geoscientific model development · doi:10.5194/gmd-17-7915-2024 · Cited by 72
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Pasche, O. C., Wider, J., Zhang, Z., Jakob Zscheischler, A., & Engelke, S. (2024) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-24-0033.1 · Cited by 54
A data-to-forecast machine learning system for global weather
Sun, X., Zhong, X., Xu, X., Huang, Y., Li, H., Neelin, J. D., Chen, D., Feng, J., Han, W., Wu, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62024-1 · Cited by 61
Improving Typhoon Predictions by Integrating Data‐Driven Machine Learning Model With Physics Model Based on the Spectral Nudging and Data Assimilation
Niu, Z., Huang, W., Zhang, L., Deng, L., Wang, H., Yang, Y., Wang, D., & Li, H. (2025) · Earth and Space Science · doi:10.1029/2024ea003952 · Cited by 35
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., & Kunstmann, H. G. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-2023-2025 · Cited by 37
Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction
Govett, M., Bah, B., Bauer, P., Bérod, D. D., Bouchet, V. S., Corti, S., Davis, C. A., Duan, Y., Graham, T. R., Honda, Y., et al. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0220.1 · Cited by 31
Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework
Niu, Z., Wang, D., Mu, M., Huang, W., Fan, X., Yang, M., & Qin, B. (2025) · Geophysical Research Letters · doi:10.1029/2025gl115926 · Cited by 24
A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track
Pu, J., Mu, M., Feng, J., Zhong, X., & Li, H. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-01009-9 · Cited by 26
Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia
Baño‐Medina, J., Sengupta, A., Doyle, J. D., Reynolds, C. A., Watson‐Parris, D., & Monache, L. D. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-00949-6 · Cited by 41
A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
Cresswell‐Clay, N., Liu, B., Durran, D. R., Liu, Z., Espinosa, Z. I., Moreno, R. A., & Karlbauer, M. (2025) · AGU Advances · doi:10.1029/2025av001706 · Cited by 68
Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data
Kent, C., Scaife, A. A., Dunstone, N., Smith, D. M., Hardiman, S. C., Dunstan, T., & Watt‐Meyer, O. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-01198-3 · Cited by 31
An artificial intelligence-based limited area model for forecasting of surface meteorological variables
Xu, P., Zheng, X., Gao, T., Wang, Y., Yin, J., Zhang, J., Zhang, X., San, L., Wang, Z., Zhang, Z., et al. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02347-5 · Cited by 31
Selected Topics in Time Series Forecasting: Statistical Models vs. Machine Learning
Tjøstheim, D. (2025) · Entropy · doi:10.3390/e27030279 · Cited by 20
A Global ERA5-based Tropical Cyclone Wind Field Dataset Enhanced by Integrated Parametric Correction Methods
Liu, G., Jiang, S., Zheng, M., Lin, S., Kong, Y., & Zhan, P. (2025) · Scientific Data · doi:10.1038/s41597-025-05789-w · Cited by 17
Assimilating Observed Surface Pressure Into ML Weather Prediction Models
Slivinski, L., Whitaker, J. S., Frolov, S., Smith, T. A., & Agarwal, N. (2025) · Geophysical Research Letters · doi:10.1029/2024gl114396 · Cited by 26
District-level solar forecasting and green hydrogen cost mapping in Türkiye using XGBoost machine learning method
Erbay, C. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.150993 · Cited by 14
AI‐Driven Weather Forecasts to Accelerate Climate Change Attribution of Heatwaves
Jiménez‐Esteve, B., Barriopedro, D., Johnson, J. E., & García‐Herrera, R. (2025) · Earth s Future · doi:10.1029/2025ef006453 · Cited by 24
A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution
Lockwood, J. W., Gori, A., & Gentine, P. (2024) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2024jh000375 · Cited by 18
Development of an offline and online hybrid model for the Integrated Forecasting System
Farchi, A., Chrust, M., Bocquet, M., & Bonavita, M. (2025) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4934 · Cited by 23
Self-citation
Physics-based models outperform AI weather forecasts of record-breaking extremes
Zhang, Z., Fischer, E. M., Jakob Zscheischler, A., & Engelke, S. (2026) · Science Advances · doi:10.1126/sciadv.aec1433 · Cited by 56
Lightning‐Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI‐Based Weather Models
Feldmann, M., Beucler, T., Gomez, M. S., & Martius, O. (2024) · Geophysical Research Letters · doi:10.1029/2024gl110960 · Cited by 15
Leveraging Data-Driven Weather Models for Improving Numerical Weather Prediction Skill through Large-Scale Spectral Nudging
Husain, S. Z., Šeparović, L., Caron, J.-F., Aider, R., Buehner, M., Chamberland, S., Lapalme, E., McTaggart‐Cowan, R., Subich, C. J., Vaillancourt, P., et al. (2025) · Weather and Forecasting · doi:10.1175/waf-d-24-0139.1 · Cited by 27
AIFS Single 1.1.0: an update to ECMWF's machine-learned weather forecast model AIFS
Moldovan, G. I., Pinnington, E., Nemesio, A. P., Lang, S. T. K., Bouallègue, Z. B., Dramsch, J. S., Alexe, M., Cruz, M. S., Hahner, S., Cook, H., et al. (2026) · Geoscientific model development · doi:10.5194/gmd-19-4703-2026 · Cited by 27
The new paradigm of computational fluid dynamics: Empowering computational fluid dynamics with machine learning
Hu, S., Jin, Q., Gao, C., Zhang, X., Lu, M., He, Y., Chu, D., & Bai, W. (2025) · Physics of Fluids · doi:10.1063/5.0280743 · Cited by 15
Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models
Ullrich, P. A., Barnes, E. A., Collins, W. D., Dagon, K., Duan, S., Elms, J., Lee, J., Leung, L.-Y. R., Lu, D., Molina, M. J., et al. (2025) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2024jh000496 · Cited by 17
Data assimilation for prediction of ammonium in wastewater treatment plant: From physical to data driven models
Bertret, V., Latimier, R. L. G., & Monbet, V. (2025) · Water Research · doi:10.1016/j.watres.2025.123673 · Cited by 10
Evaluating AI’s capability to reflect physical mechanisms: a case study of tropical cyclone impacts on extreme rainfall
Xu, H., Duan, Y., & Xu, X. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad6fbb · Cited by 10
Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics
Sahu, P. L., Sandeep, S., & Kodamana, H. (2025) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2025jh000594 · Cited by 16
Intelligent long-term prediction of wind-induced response of super high-rise buildings during typhoons based on wind tunnel tests
Li, F., Xie, Z., Zhang, L., Yu, X., & Shi, B. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112144 · Cited by 7
The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability
Zhou, L., & Zhang, R. (2025) · Journal of Oceanology and Limnology · doi:10.1007/s00343-025-4330-y · Cited by 7
Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective
Ding, J., Chen, W., Chen, J., Wang, J., Zhang, Y., Bai, L., Wang, Y., Mi, X., Liu, T., & Weng, D. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104473 · Cited by 9
The ecological forecast limit revisited: Potential, absolute and relative system predictability
Wesselkamp, M., Albrecht, J., Pinnington, E., Castillo, W. J., Pappenberger, F., & Dormann, C. F. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70049 · Cited by 6
AI for atmosphere–ocean sciences: advancements, challenges and ways forward
Luo, J., Xia, J., Pan, B., Ham, Y.-G., Li, X., Shangguan, W., Xue, W., Wang, Y., Mu, B., Hong, Y., et al. (2026) · National Science Review · doi:10.1093/nsr/nwag063 · Cited by 13
A predictive model for the estimation of industrial PM2.5 emissions for IoT-based devices
Kychkin, A. V., Леонидовна, В. О., Mylnikov, L., & Chernitsin, I. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110662 · Cited by 6
Ensemble data assimilation to diagnose AI-based weather prediction models: a case with ClimaX version 0.3.1
Kotsuki, S., Shiraishi, K., & Okazaki, A. (2025) · Geoscientific model development · doi:10.5194/gmd-18-7215-2025 · Cited by 9
Temporal pattern-aware temperature forecasting using CatBoost: A hybrid machine learning approach
Mouad, B. H. I. H., Elassad, Z. E. A., Siti, H., Meslouhi, O. E., Abouelmehdi, K., & Boustani, A. E. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110212 · Cited by 5
An extension of WeatherBench 2 to binary hydroclimatic forecasts
Zhao, T., Li, Q., Tu, T., & Chen, X. (2025) · Geoscientific model development · doi:10.5194/gmd-18-5781-2025 · Cited by 7
Benchmarking Atmospheric Circulation Variability in an AI Emulator, ACE2, and a Hybrid Model, NeuralGCM
Baxter, I., Pahlavan, H. A., Hassanzadeh, P., Rucker, K., & Shaw, T. A. (2026) · Geophysical Research Letters · doi:10.1029/2025gl119877 · Cited by 7
Binned spectral power loss for improved prediction of chaotic systems
Chakraborty, D., Mohan, A. T., & Maulik, R. (2026) · Journal of Computational Physics · doi:10.1016/j.jcp.2026.114866 · Cited by 10
Solving coastal dynamics: introduction to high-resolution ocean forecasting services
Staneva, J. V., Melet, A., Veitch, J., & Matte, P. (2025) · State of the Planet · doi:10.5194/sp-5-opsr-4-2025 · Cited by 4
Impact of VAEformer Compression Algorithm Precision Loss on the Tropospheric Delays for Microwave Remote Sensing
Ding, J., Xu, C., Chen, W., Chen, J., Wang, J., Zhang, Y., Bai, L., Han, T., & Xiong, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3587944 · Cited by 5
Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western US
Ryu, J., Kim, H., Wang, S.-Y. (., & Yoon, J. (2026) · Geoscientific model development · doi:10.5194/gmd-19-27-2026 · Cited by 5
On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed
Pahlavan, H. A., Hassanzadeh, P., & Alexander, M. J. (2025) · Geophysical Research Letters · doi:10.1029/2024gl114136 · Cited by 4
U‐Net Kalman Filter (UNetKF): An Example of Machine Learning‐Assisted Data Assimilation
Lu, F. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms003979 · Cited by 5
AI Merged With Human Knowledge Produces the Best Possible Weather Forecasts
Penny, S. G. (2025) · Geophysical Research Letters · doi:10.1029/2025gl116465 · Cited by 3
Global Ensemble Weather Prediction from a Deep Learning–Based Model (Pangu-Weather) with the Initial Condition Perturbations of CMA-GEPS
Liu, X., Chen, J., Zhu, Y., Liu, Y., Chen, F., Huo, Z., Peng, F., Ma, Y., & Gong, Y. (2025) · Advances in Atmospheric Sciences · doi:10.1007/s00376-024-4173-z · Cited by 4
Harnessing AI Data-Driven Global Weather Models for Climate Attribution: An Analysis of the 2017 Oroville Dam Extreme Atmospheric River
Baño‐Medina, J., Sengupta, A., Michaelis, A., Monache, L. D., Kalansky, J. F., & Watson‐Parris, D. (2025) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-24-0090.1 · Cited by 6
Advances in atmospheric, oceanic, and coupled models for meteorological forecasting
Waqas, M., Humphries, U. W., & Wangwongchai, A. (2025) · Natural Hazards Research · doi:10.1016/j.nhres.2025.10.003 · Cited by 3
LUCIE: A Lightweight Uncoupled Climate Emulator With Long‐Term Stability and Physical Consistency
Guan, H., Arcomano, T., Chattopadhyay, A., & Maulik, R. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2025ms005152 · Cited by 30
Verification of AI–based environmental forecasting systems: What can we do, what do we need to do, and what are the challenges?
Bröcker, J., Driscoll, S., Necker, T., Rodríguez, J. M., Dacre, H., Harvey, N. J., & Bouallègue, Z. B. (2026) · Journal of the European Meteorological Society. · doi:10.1016/j.jemets.2026.100032 · Cited by 8
ArchesWeatherGen: Skillful and compute-efficient probabilistic weather forecasting with machine learning
Couairon, G., Singh, R., Charantonis, A., Lessig, C., & Monteleoni, C. (2026) · Science Advances · doi:10.1126/sciadv.adx2372 · Cited by 62
The convergence of machine learning and data assimilation in Earth system science
Arcucci, R., Healy, S. B., Dance, S. L., Lei, L., Bach, E., Weaver, A., Miyoshi, T., Dillon, M. E., Draper, C. S., Schneider, R., et al. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00107-0 · Cited by 5
Self-citation
Diagnosing lateral boundary spin‐up in regional models using an age‐of‐air diagnostic
Warner, J., Franklin, C. N., Roux, B., Cooper, S., Rennie, S. J., & Kumar, V. (2025) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4971 · Cited by 3
Deep Learning for Atmospheric Modeling: A Proof of Concept Using a Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes
Rocha, P. A. C., Thé, J. V. G., Santos, V. O., & Gharabaghi, B. (2025) · Atmosphere · doi:10.3390/atmos16040394 · Cited by 3
Machine learning for subseasonal-to-seasonal prediction
Mayer, K. J., Lerch, S., de Burgh-Day, C., & Arcodia, M. C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-31538-1.00003-8 · Cited by 3
Improving the hindcast of late spring-early summer heatwaves in Southeast Asia with machine learning models
Gui, S., Yang, Z., Wu, H., Yang, R., Dong, Z., Yang, Y., & Lian, Y. (2025) · Climate Dynamics · doi:10.1007/s00382-025-07781-x · Cited by 3
Modulation of tropical cyclogenesis on subseasonal-to-interannual timescales in the deep-learning climate emulator ACE2
Chien, M., Barnes, E. A., & Maloney, E. D. (2025) · Machine Learning Earth · doi:10.1088/3049-4753/adfd61 · Cited by 9
A graph neural networks approach predicted spatiotemporal changes of ozone concentrations in the Yangtze River Delta (China)
Lin, Z., Wang, S., Xu, J., Shi, P., Ma, Y., Wang, Y., & Zhang, G. (2025) · Environmental Research Communications · doi:10.1088/2515-7620/ae0012 · Cited by 2
Neural network approaches for generation forecasting and conceptual design in hybrid renewable energy systems
Kavaliauskas, Ž., Milieška, M., Blažiūnas, G., Gecevičius, G., & Zhairabany, H. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111241 · Cited by 2
A dual-branch typhoon-induced wave height forecasting network with tail-aware extreme value optimization
Lv, T., Yu, H., Wang, H., Zhu, Y., & Lu, L. (2025) · Weather and Climate Extremes · doi:10.1016/j.wace.2025.100844 · Cited by 2
A Unified Neural Background‐Error Covariance Model for Midlatitude and Tropical Atmospheric Data Assimilation
Melinc, B., Perkan, U., & Zaplotnik, Ž. (2026) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2025ms005360 · Cited by 2
Physics‐Based Versus AI Weather Prediction Models: A Comparative Performance Assessment of Atmospheric River Prediction
Davis, I., Subramanian, A. C., Higgins, T. B., Sengupta, A., & Monache, L. D. (2026) · Geophysical Research Letters · doi:10.1029/2025gl117609 · Cited by 7
Data-driven forecasts of extreme weather in East Asia: feasibility of operational use
Oh, S.-G., Bae, Y.-J., Son, S., Hong, D., Kim, J.-Y., Kim, Y., Yoon, H., Yoon, J., Jeong, J., Kim, H., et al. (2026) · Weather and Climate Extremes · doi:10.1016/j.wace.2026.100875 · Cited by 2
Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model
Chen, Z., Leung, L.-Y. R., Zhou, W., Lu, J., Lubis, S. W., Liu, Y., Chang, C.-C., Harrop, B. E., Wang, Y., Yang, M. M., et al. (2026) · AGU Advances · doi:10.1029/2025av002075 · Cited by 3
A spectral test of the butterfly effect and physical consistency in the diffusion-based GenCast’s ensembles
Kim, H., Ryu, J., Son, S., Jeong, J., Kim, H., & Yoon, J. (2026) · npj Climate and Atmospheric Science · doi:10.1038/s41612-026-01380-1 · Cited by 4
Datasheets for Earth Science Datasets
Connolly, C. J., Hueholt, D. M., & Burt, M. (2025) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-24-0203.1 · Cited by 2
A Big Chill: Examining an Extremely Sharp Cold Front from Three Perspectives
Schumacher, R. S., Mazurek, A. C., Davis, C. M., Franke, M. E., & Gregg, M. (2025) · Monthly Weather Review · doi:10.1175/mwr-d-24-0132.1 · Cited by 3
Closing the Gap—Hurricane Prediction Advances in the U.S. FV3-Based Models
Chen, J., Marchok, T. P., Bender, M. A., Gao, K., Gopalakrishnan, S., Harris, L., Hazelton, A. T., Liu, B., Mehra, A., Morin, M., et al. (2025) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-24-0036.1 · Cited by 1
GDOSphere: A spherical graph neural network framework with neural operators for weather forecasting
Xu, Z., Pan, B., Wei, X., Li, H., Tian, D., & Li, Z. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130772 · Cited by 1
Optimization of Parallel Fourier Transform in YHGSM Based on Computation–Communication Overlap
Zheng, Y., Wu, J., Chen, T., Yang, J., Yin, F., & Chen, X. (2025) · Electronics · doi:10.3390/electronics14163238 · Cited by 1
A Hybrid Approach for Seasonal Rainfall Forecasting Across Vietnam Using Convolutional Neural Networks and Dynamical Downscaling
Ngoc, T. P., Nguyen, H. D., Trinh‐Tuan, L., Nguyen-Duc, P., Trinh, T., & Phan‐Van, T. (2025) · Pure and Applied Geophysics · doi:10.1007/s00024-025-03838-4 · Cited by 1
A Fine‐Tuned Pangu Weather Model and Its Performance Based on an Operational Framework in South China
Xin, X., Gao, Y., Lu, C., Wang, W., Li, Y., Wan, Q., Li, C., Zhang, C., You, H., & Chen, X. (2025) · Meteorological Applications · doi:10.1002/met.70114 · Cited by 1
Can data-driven weather models accurately forecast atmospheric rivers?
López-Martí, F., Olivetti, L., Vallejo‐Bernal, S. M., Rutgersson, A., & Messori, G. (2025) · Environmental Research Letters · doi:10.1088/1748-9326/ae1e8e · Cited by 4
Dynamical errors in machine learning forecasts
Fang, Z., & Mengaldo, G. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117376 · Cited by 5
Whose weather is it? A fairness framework for data-driven weather forecasting
Olivetti, L., & Messori, G. (2025) · Environmental Research Letters · doi:10.1088/1748-9326/ae21f5 · Cited by 2
Investigation of Impact of Satellite Remote Sensing Data on the AI Weather Prediction Coupling with Physics-Based Data Assimilation
Dong, P., Zhang, S., Kan, W., & Zhang, X. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242424 · Cited by 1
SynQPF‐Net: Short‐Term Precipitation Forecasts by Integrating GraphCast Predictions and High‐Resolution Observational Analyses
Chen, D., Yao, D., & Wang, Y. (2026) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2025jh000907 · Cited by 1
Evaluating Machine Learning Weather Models for Data Assimilation: Fundamental Limitations in Tangent Linear and Adjoint Properties
Tian, X., Holdaway, D., & Kleist, D. T. (2026) · Geophysical Research Letters · doi:10.1029/2025gl119402 · Cited by 6
Hybrid Ensemble Model for Advanced Climate Prediction
Narayanan, A., Narayana, P. V., & Rama Parvathy, L. (2025) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon65210.2025.11341641 · Cited by 1
Hybridizing deep learning models and a chemical transport model for medium-term PM2.5 forecasts in the Yangtze River Delta, China
Zhu, M., Wu, L., Qi, L., Wang, Z., Cao, W., Zhang, S., Pan, X., Li, J., Xin, J., Sun, Y., et al. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.01.080 · Cited by 1
Comparative evaluation of data‐driven weather forecast models performance for medium‐ to extended‐range weather forecasting and tropical cyclone genesis in 2024
Ho, C. K., Leung, M. Y. T., He, Y. H., & Chan, P. W. (2026) · Weather · doi:10.1002/wea.70043 · Cited by 1
A hybrid framework for global weather forecasting via low-resolution dynamical core and multigrid neural operator
Yifan, H. U., Yin, F., Zhang, W., Ren, K., Song, J., & Deng, K. (2026) · npj Climate and Atmospheric Science · doi:10.1038/s41612-026-01374-z · Cited by 2
Hybrid physics–data‐driven modeling for sea ice thermodynamics and transfer learning
Cillis, G. D., Carrassi, A., Brajard, J., Bertino, L., Broccoli, M., Iovino, D., Finn, T. S., & Bocquet, M. (2026) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.70200 · Cited by 2
Intercomparison of AI forecasts and numerical weather predictions for the July 2021 extreme precipitation event in Henan, China
Li, Q., & Zhao, T. (2026) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-026-03271-6 · Cited by 2
Probing Atmospheric Gravity‐Wave‐Relevant Representations in a Weather Foundation Model via Multi‐Method Fine‐Tuning
Guo, Y., Song, J., Zhao, C., Cao, X., Yang, Y., & Leng, H. (2026) · Geophysical Research Letters · doi:10.1029/2026gl124619
Machine Learning for Heatwave Prediction: A Global Scoping Review of Environmental Predictors and Modelling Practices
Ashford, A., Ayaz, F., Shakir, M. Z., Ramzan, N., Gebreslasie, M. T., Viriri, S., Ndzi, D. L., Dickinson, N., Spencer, L. H., Lynch, M., et al. (2026) · Forecasting · doi:10.3390/forecast8040063
2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction
Ebel, P., Schneider, R., Bonavita, M., Clare, M., Jungbluth, A., Pourshamsi, M., Chantry, M., Alexe, M., Sebastianelli, A., & Chrust, M. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00757-4 · Cited by 3
Self-citation
The Impact of Tropical Convective Heating on the Amundsen Sea Low in a Deep-Learning Weather Forecasting Model
Huang, Y., Zhou, Z., Zhang, R., Zhong, X., & Li, H. (2025) · Advances in Atmospheric Sciences · doi:10.1007/s00376-025-4360-6 · Cited by 2
Generating ensembles of spatially coherent in situ forecasts using flow matching
Landry, D. A., Monteleoni, C., & Charantonis, A. (2025) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.70055 · Cited by 1
Datasheets for Earth Science DatasetsPreprint
Hueholt, D. M., Connolly, C. J., & Burt, M. (2024) · doi:10.31219/osf.io/hwa3p · Cited by 1
Comment on hess-2024-212
(2024) · Hydrology and earth system sciences · doi:10.5194/hess-2024-212-rc1
Reply on RC1
Dong, N. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-2024-212-ac1
Summary and future prospects
Rajeevan, M. N., Mukhopadhyay, P., & Chakraborty, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23945-8.00010-0
Harnessing dynamic graph differential operators for efficient data-driven wind prediction
Wei, X., Xu, Z., Li, H., Hao, J., Yue, H., & Liu, C. (2025) · GeoInformatica · doi:10.1007/s10707-025-00542-2
A Spatiotemporal Machine Learning Framework for the Prediction of Metocean Conditions in the Gulf of Mexico: Application to Loop Current and Loop Current Eddy Forecasting
Steele, E. C. C., Juniper, M. C. R., Pillai, A. C., Ashton, I. G. C., Chen, J., Jaramillo, S., & Zarate, L. (2025) · Offshore Technology Conference · doi:10.4043/35733-ms · Cited by 1

Authors: Bonavita, M.
Affiliations: European Centre for Medium-Range Weather Forecasts (GB)
Corresponding author: Bonavita, Massimo
ORCID: Bonavita, Massimo
Venue: Geophysical Research Letters
Publisher: American Geophysical Union (AGU)
Published: 2024-06-14
Volume: 51
Issue: 12
ISSN: 0094-8276 (ISSN-L)
1944-8007
DOI: 10.1029/2023gl107377
OpenAlex: W4399668152
Cited by: 155
References: 13
Citations by year: 2026: 68 · 2025: 73 · 2024: 14
FWCI: 28.29
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Geophysical Research Letters (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords: machine learning weather prediction, Pangu-Weather, FourCastNet, GraphCast, physical consistency, forecast skill, physics-based modeling, data-driven prediction models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

On Some Limitations of Current Machine Learning Weather Prediction Models

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks