paper
2020
Hybrid physics-ML & parameterization
Climate emulation Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)CNN / U-Net Other
2020-01-15
016
Jan 2020
Garrett C Limon, Christiane Jablonowski
preprint
Random forests, linear regression and CNN-based networks emulate CAM6 physical forcings offline; tuning is needed to capture detailed patterns, with online coupling left for future work.
Hybrid physics-ML & parameterization
An Assessment of Machine Learning Techniques for Replicating Physical Forcing Mechanisms in Climate Models
In one sentence. Random forests, linear regression and CNN-based networks emulate CAM6 physical forcings offline; tuning is needed to capture detailed patterns, with online coupling left for future work.
Labels
- Topic
- Hybrid physics-ML & parameterization, Climate emulation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels), CNN / U-Net
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1002/essoar.10501799.1 OpenAlex:W3049610443
- Added
- 2026-10-22
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