CoChat
Sign Up
Cited by 114Meteorological Phenomena and SimulationsHydrological Forecasting Using AITraffic Prediction and Management Techniques

The DOI matches its Crossref record; 161 of the 167 references it cites were found in a registry or an index, and 6 are not checked yet.

Machine Learning Methods for Weather Forecasting: A Survey

Open access
Zhang, Huijun, Liu, Yaxin, Zhang, Chongyu, Li, Ningyun·2025·Atmosphere
DOI 10.3390/atmos16010082
Open this paper in CoChat — free accountOpen PDF

Abstract

Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/atmos16010082

Cites

161 of 167 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 15

  1. Holmstrom, 2016, Machine learning applied to weather forecasting, Meteorol. Appl., 10, 1
  2. Karevan, Z., and Suykens, J.A. (2018). Spatio-temporal stacked LSTM for temperature prediction in weather forecasting. arXiv.
  3. Kim, 2023, Comparison of deep learning-based models for forecasting precipitation using era-5 and radar images, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, Volume 12727, 11
  4. Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.J., Chen, X., Ma, L., Zhang, T., and Su, R. (2023). Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead. arXiv.
  5. Han, T., Guo, S., Ling, F., Chen, K., Gong, J., Luo, J., Gu, J., Dai, K., Ouyang, W., and Bai, L. (2024). Fengwu-ghr: Learning the kilometer-scale medium-range global weather forecasting. arXiv.
  6. Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J.K., and Grover, A. (2023). ClimaX: A foundation model for weather and climate. arXiv.
  7. Bodnar, C., Bruinsma, W.P., Lucic, A., Stanley, M., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J., Dong, H., and Vaughan, A. (2024). Aurora: A foundation model of the atmosphere. arXiv.
  8. Keisler, R. (2022). Forecasting global weather with graph neural networks. arXiv.
  9. Kingma, D.P. (2013). Auto-encoding variational bayes. arXiv.
  10. Zhong, X., Chen, L., Li, H., Feng, J., and Lu, B. (2024). FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting. arXiv.
  11. Sønderby, C.K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N. (2020). Metnet: A neural weather model for precipitation forecasting. arXiv.
  12. Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A. (2020). Fourier neural operator for parametric partial differential equations. arXiv.
  13. Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., and Azizzadenesheli, K. (2022). Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv.
  14. Guibas, J., Mardani, M., Li, Z., Tao, A., Anandkumar, A., and Catanzaro, B. (2021, January 4). Efficient token mixing for transformers via adaptive fourier neural operators. Proceedings of the International Conference on Learning Representations, Vienna, Austria.
  15. Xiang, Y., Jin, W., Dong, H., Bai, M., Fang, Z., Zhao, P., Sun, H., Thambiratnam, K., Zhang, Q., and Huang, X. (2024). ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting. arXiv.

Cited by

Sorted by citations

See all 114

Related

See all

Metadata

Authors
Zhang, H., Liu, Y., Zhang, C., & Li, N.
Affiliations
  • Huaneng Clean Energy Research Institute (CN)
  • Beijing Big Data Center, No. 3 Courtyard, Liuzhuang Road, Tongzhou District, Beijing 101117, China
Corresponding author
Zhang, Huijun
ORCID
Venue
Atmosphere
Publisher
MDPI AG
Published
2025-01-14
Volume
16
Issue
1
Pages
82
ISSN
2073-4433 (ISSN-L)
DOI
10.3390/atmos16010082
OpenAlex
W4406362609
Cited by
113
References
162
Citations by year
2026: 61 · 2025: 52
FWCI
49.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Atmosphere (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
China Huaneng Clean Energy Research Institute (QNYJJ24-02)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
Keywords
global weather forecasting, downscaling, extreme weather prediction, model interpretability

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