Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
<p>Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of subgrid processes in Earth system models (ESMs). So far, all studies were based on the same three-step approach: first a training dataset was created from a hig...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-05-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/13/2185/2020/gmd-13-2185-2020.pdf |