The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere
Open accessAbstract
We present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an off‐line volume‐conservative mapping to a cubed‐sphere grid, improvements to the CNN architecture and the minimization of the loss function over multiple steps in a prediction sequence. The cubed‐sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short‐ to medium‐range forecasting, our model significantly outperforms persistence, climatology, and a coarse‐resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high‐resolution state‐of‐the‐art operational NWP system. Our data‐driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top‐of‐atmosphere solar forcing. Although it currently does not compete with operational weather forecasting models, our data‐driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large‐ensemble forecasting.
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Cites
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Cited by
Learning skillful medium-range global weather forecasting
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., et al. (2023) · Science · doi:10.1126/science.adi2336 · Cited by 1,466
Neural general circulation models for weather and climate
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454
“…janniyuval@google.com; shoyer@google.com although there has been some success in using machine-learning approaches on longer timescales 17,18 , these models have not demonstrated the ability to outperform existing GCMs. Hybrid models that combine GCMs with machine learning are…”
BackgroundFuXi: a cascade machine learning forecasting system for 15-day global weather forecast
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., & Li, H. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00512-1 · Cited by 367
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
WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez‐Gonzalez, A., Yang, V., Carver, R., Agrawal, S., et al. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004019 · Cited by 245
Metadata
- Authors
- Weyn, J. A., Durran, D. R., & Caruana, R.
- Venue
- Journal of Advances in Modeling Earth Systems
- Publisher
- American Geophysical Union (AGU)
- Published
- 2020-09
- Volume
- 12
- Issue
- 9
- ISSN
- 1942-2466 (ISSN-L)
- OpenAlex
- W3048374358
- Cited by
- 336
- References
- 38
- FWCI
- 13.16
- Type
- journal article
- Language
- en
- Open access
- gold
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