The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Deep learning for twelve hour precipitation forecasts
Open accessAbstract
Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasing the resolution underlying the simulation, which can be computationally prohibitive. An emerging class of weather models based on neural networks overcome these limitations by learning the required transformations from data instead of relying on hand-coded physics and by running efficiently in parallel. Here we present a neural network capable of predicting precipitation at a high resolution up to 12 h ahead. The model predicts raw precipitation targets and outperforms for up to 12 h of lead time state-of-the-art physics-based models currently operating in the Continental United States. The results represent a substantial step towards validating the new class of neural weather models.
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Cites
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087
The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490
A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R., Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A., et al. (2016) · Monthly Weather Review · doi:10.1175/mwr-d-15-0242.1 · Cited by 1,164
Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03854-z · Cited by 1,024
Neural Machine Translation in Linear Time
Kalchbrenner, N., Espeholt, L., Simonyan, K., Oord, A. V. D., Graves, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1610.10099 · Cited by 414
Self-citationMetNet: A Neural Weather Model for Precipitation Forecasting
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 291
Self-citationMachine Learning for Precipitation Nowcasting from Radar Images
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., & Hickey, J. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12132 · Cited by 226
Self-citationGSPMD: General and Scalable Parallelization for ML Computation GraphsPreprint
Xu, Y., Lee, H., Chen, D., Hechtman, B. A., Huang, Y., Joshi, R., Krikun, M., Lepikhin, D., Ly, A., Maggioni, M., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04663 · Cited by 197
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Skilful nowcasting of extreme precipitation with NowcastNet
Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., & Wang, J. (2023) · Nature · doi:10.1038/s41586-023-06184-4 · Cited by 465
Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123537 · Cited by 303
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
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Metadata
- Authors
- Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J., Bell, A., & Kalchbrenner, N.
- Venue
- Nature Communications
- Published
- 2022-09-01
- Volume
- 13
- Issue
- 1
- Pages
- 5145-5145
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4294068833
- Cited by
- 253
- References
- 12
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- gold
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- Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J., Bell, A., & Kalchbrenner, N. (2022). Deep learning for twelve hour precipitation forecasts. Nature Communications. https://doi.org/10.1038/s41467-022-32483-x
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