The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
Open accessAbstract
In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.
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.
Integrity signals
Registry
Cites
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.
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
Deep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673
Introduction to the Theory of Neural Computation
Hertz, J., Krogh, A., Palmer, R. G., & Horner, H. (1991) · Physics Today · doi:10.1063/1.2810360 · Cited by 1,079
tidytext: Text Mining and Analysis Using Tidy Data Principles in R
Silge, J., & Robinson, D. (2016) · The Journal of Open Source Software · doi:10.21105/joss.00037 · Cited by 941
Classifications of Atmospheric Circulation Patterns: Recent Advances and Applications
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., & Tveito, O. E. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1446.019 · Cited by 619
Self-citationGordon, A. (1999) · Chapman & Hall/CRC eBooks · doi:10.1201/9780367805302 · Cited by 619
Downscaling of precipitation for climate change scenarios: A support vector machine approach
Tripathi, S., Srinivas, V. V., & Nanjundiah, R. S. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.04.030 · Cited by 607
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497
Machine learning and artificial intelligence to aid climate change research and preparedness
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab4e55 · Cited by 469
O'Gorman, P. A., & Dwyer, J. G. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001351 · Cited by 419
Schneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017) · Geophysical Research Letters · doi:10.1002/2017gl076101 · Cited by 391
Cited by
Interpretable machine learning for weather and climate prediction: A review
Yang, R., Hu, J., Li, Z., Mu, J., Yu, T., Xia, J., Li, X., Dasgupta, A., & Xiong, H. (2024) · Atmospheric Environment · doi:10.1016/j.atmosenv.2024.120797 · Cited by 139
Boudreault, J., Lamothe, F., Campagna, C., & Chebana, F. (2025) · Environment International · doi:10.1016/j.envint.2025.109965 · Cited by 10
Metadata
- Authors
- Bochenek, B., & Ustrnul, Z.
- Venue
- Atmosphere
- Publisher
- MDPI AG
- Published
- 2022-01-23
- Volume
- 13
- Issue
- 2
- Pages
- 180
- ISSN
- 2073-4433 (ISSN-L)
- OpenAlex
- W4206973253
- Cited by
- 325
- References
- 73
- FWCI
- 25.88
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives. https://app.cochat.ai/papers/10.3390/atmos13020180
- The paper
- Bochenek, B., & Ustrnul, Z. (2022). Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives. Atmosphere. https://doi.org/10.3390/atmos13020180
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
