paper
2019
Climate analysis & attribution
Review, survey or tutorial
OtherLawrence Berkeley National Laboratory / NERSCMax Planck Institute for BiogeochemistryMax Planck Institute for Meteorology Journal
★ Highlight 2019-02-13
078
Feb 2019
Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens et al.
Nature
Lawrence Berkeley National Laboratory / NERSC, Max Planck Institute for Biogeochemistry, Max Planck Institute for Meteorology
Nature review on deep learning for data-driven Earth system science, covering weather, climate and process understanding; abstract absent, judged from title and venue.
1
Climate analysis & attribution★ Highlight
Deep learning and process understanding for data-driven Earth system science
Why it matters. Widely cited Nature perspective defining how deep learning and process understanding should combine in Earth system science.
In one sentence. Nature review on deep learning for data-driven Earth system science, covering weather, climate and process understanding; abstract absent, judged from title and venue.
Labels
- Topic
- Climate analysis & attribution
- Type
- Review, survey or tutorial
- Method
- Other
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1038/s41586-019-0912-1 OpenAlex:W2913323966
- Added
- 2026-10-22
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