The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deep learning for multi-year ENSO forecasts
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150
Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98
Webster, P. J., Moore, A. M., Loschnigg, J. P., & Leben, R. R. (1999) · Nature · doi:10.1038/43848 · Cited by 2,144
Yeh, S.-W., Kug, J.-S., Dewitte, B., Kwon, M.-H., Kirtman, B. P., & Jin, F.-F. (2009) · Nature · doi:10.1038/nature08316 · Cited by 1,592
Cited by
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002203 · Cited by 663
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01294-x · Cited by 552
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · doi:10.2139/ssrn.3804983 · Cited by 552
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308
Machine Learning Methods in Weather and Climate Applications: A Survey
Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z. (2023) · doi:10.20944/preprints202309.1764.v2 · Cited by 246
Metadata
- Authors
- Ham, Y.-G., Kim, J.-H., & Luo, J.-J.
- Venue
- Nature
- Published
- 2019-09-18
- Volume
- 573
- Issue
- 7775
- Pages
- 568-572
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2973731563
- Cited by
- 1,261
- References
- 8
- FWCI
- 53.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Ham, Y.-G., Kim, J.-H., & Luo, J.-J. (2019). Deep learning for multi-year ENSO forecasts. Nature. https://doi.org/10.1038/s41586-019-1559-7
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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.
