CoChat
Sign Up
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.

Overview

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.

Verification

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?

Citations

Cites

Show all 13 references

Research works printed but not linked: 17

  1. 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.
  2. 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
  3. 10.1038/s41586‐023‐06185‐3 · DOI · Paper page
  4. 10.1038/s41612‐023‐00387‐2 · DOI
  5. 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
  6. 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
  7. Keisler R.(2022).Forecasting global weather with graph neural networks. arXiv preprint arXiv:2202.07575. · arXiv
  8. Knapp K. R., 2018, International best track archive for climate stewardship (IBTrACS) project, version 4
  9. Krasnopolsky V., 2023, Review: Using machine learning for data assimilation, model physics, and post‐processing model outputs, 513
  10. 10.1175/1520‐0493(1974)102<0409:tsomcf>2.0.co;2 · DOI
  11. 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
  12. Sanchez‐Gonzalez A., 2020, International Conference on Machine Learning, 8459
  13. 10.1038/s41612‐022‐00269‐z · DOI
  14. 10.22541/essoar.169111361.10601606/v1 · DOI
  15. 10.1175/15200493(1997)125<3297:EFANAT>2.0.CO;2 · DOI
  16. 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
  17. 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

Cited by

Sorted by citations

Items 101–155 of 155

Related

See all

Metadata

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
Licence
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