One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Nørgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James W. Lottes, Stephan Rasp, Peter D. Düben and 6 more
General circulation models (GCMs) are the foundation of weather and climate prediction 1,2 . GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting 3,4 . However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Kochkov, D., Yuval, J., Langmore, I., Nørgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J. W., Rasp, S., Düben, P. D., Hatfield, S., Battaglia, P., Sánchez‐González, Á., Willson, M., Brenner, M. P., & Hoyer, S. (2024). Neural general circulation models for weather and climate. Nature, 632(8027), 1060–1066. https://doi.org/10.1038/s41586-024-07744-y
Kochkov, Dmitrii, et al. "Neural general circulation models for weather and climate." Nature, vol. 632, no. 8027, 2024, pp. 1060–1066, https://doi.org/10.1038/s41586-024-07744-y.
Kochkov, Dmitrii, Janni Yuval, Ian Langmore, Peter Nørgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, et al. "Neural general circulation models for weather and climate." Nature 632, no. 8027 (2024): 1060–1066. https://doi.org/10.1038/s41586-024-07744-y.
Kochkov D., Yuval J., Langmore I. [и др.] Neural general circulation models for weather and climate // Nature. — 2024. — Vol. 632, № 8027. — P. 1060–1066. — URL: https://doi.org/10.1038/s41586-024-07744-y
@article{kochkov2024neural,
title = {{Neural general circulation models for weather and climate}},
author = {Dmitrii Kochkov and Janni Yuval and Ian Langmore and Peter Nørgaard and Jamie Smith and Griffin Mooers and Milan Klöwer and James W. Lottes and Stephan Rasp and Peter D. Düben and Sam Hatfield and Peter Battaglia and Álvaro Sánchez‐González and Matthew Willson and Michael P. Brenner and Stephan Hoyer},
journal = {Nature},
year = {2024},
volume = {632},
number = {8027},
pages = {1060--1066},
doi = {10.1038/s41586-024-07744-y},
url = {https://doi.org/10.1038/s41586-024-07744-y}
}TY - JOUR AU - Kochkov, Dmitrii AU - Yuval, Janni AU - Langmore, Ian AU - Nørgaard, Peter AU - Smith, Jamie AU - Mooers, Griffin AU - Klöwer, Milan AU - Lottes, James W. AU - Rasp, Stephan AU - Düben, Peter D. AU - Hatfield, Sam AU - Battaglia, Peter AU - Sánchez‐González, Álvaro AU - Willson, Matthew AU - Brenner, Michael P. AU - Hoyer, Stephan TI - Neural general circulation models for weather and climate JO - Nature VL - 632 IS - 8027 SP - 1060 EP - 1066 PY - 2024 DA - 2024/07/22 DO - 10.1038/s41586-024-07744-y UR - https://doi.org/10.1038/s41586-024-07744-y ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.