paper
2020
Hybrid physics-ML & parameterization
Review, survey or tutorial
GAN or VAE Journal
2020-04-07
008
Apr 2020
Kate Wheeling
Eos
Eos commentary on a study finding GANs can produce stochastic parameterizations that improve how weather and climate models represent unresolved subgrid processes.
Hybrid physics-ML & parameterization
Machine Learning Improves Weather and Climate Models
In one sentence. Eos commentary on a study finding GANs can produce stochastic parameterizations that improve how weather and climate models represent unresolved subgrid processes.
Abstract
New research evaluates the performance of generative adversarial networks for stochastic parameterizations.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- Review, survey or tutorial
- Method
- GAN or VAE
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2020eo142422 OpenAlex:W3015648822
- Added
- 2026-10-22
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