paper
2023
Hybrid physics-ML & parameterization
Sub-seasonal to seasonal New model or method
OtherSeasonal to annualLawrence Berkeley National Laboratory / NERSC Other
2023-03-16
022
Mar 2023
Zikang He, Julien Brajard, Yiguo Wang et al.
preprint
Lawrence Berkeley National Laboratory / NERSC
Machine learning emulates the systematic error of a reduced-order coupled ocean-atmosphere model, and the resulting hybrid model beats the dynamical model alone in twin experiments, best when correcting both components.
Hybrid physics-ML & parameterization
Improve dynamical climate prediction with machine learning
In one sentence. Machine learning emulates the systematic error of a reduced-order coupled ocean-atmosphere model, and the resulting hybrid model beats the dynamical model alone in twin experiments, best when correcting both components.
Labels
- Topic
- Hybrid physics-ML & parameterization, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Other
- Time scale
- Seasonal to annual
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.22541/essoar.167898499.91486328/v1 OpenAlex:W4327571671
- Added
- 2026-10-22
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