paper
2021
Hybrid physics-ML & parameterization
Climate analysis & attribution Review, survey or tutorial
Physics-informed / physics-constrainedOtherClimate projections / centennialColorado State University (incl. CIRA)Columbia University (LEAP, Lamont-Doherty) Other
2021-04-29
026
Apr 2021
Tom Beucler, I. Ebert-Uphoff, S. Rasp et al.
preprint
Colorado State University (incl. CIRA), Columbia University (LEAP, Lamont-Doherty)
Review of interpretable and physics-guided machine learning for cloud processes in climate models, covering radiation, microphysics, convection and cloud detection, with a getting-started guide.
Hybrid physics-ML & parameterization
Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series "Clouds and Climate")
In one sentence. Review of interpretable and physics-guided machine learning for cloud processes in climate models, covering radiation, microphysics, convection and cloud detection, with a getting-started guide.
Labels
- Topic
- Hybrid physics-ML & parameterization, Climate analysis & attribution
- Type
- Review, survey or tutorial
- Method
- Physics-informed / physics-constrained, Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output, Other
- Status
- Research
- Identifiers
- DOI:10.1002/essoar.10506925.1 OpenAlex:W3169806764
- Added
- 2026-10-22
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