Datasets for 'Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The ability of machine-learning-based (MLbased) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At the same time, ML-based emulators of existing physically based parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art general circulation model (GCM) are robust with respect to the substantial structural and parametric change in the hostmodel: when used in two 7-month-long experiments with a new GCM, they remain stable and generate realisti' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["emulation","general circulation model","neural network","radiative transfer parameterization"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The ability of machine-learning-based (MLbased) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At the same time, ML-based emulators of existing physically based parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art general circulation model (GCM) are robust with respect to the substantial structural and parametric change in the hostmodel: when used in two 7-month-long experiments with a new GCM, they remain stable and generate realisti' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The ability of machine-learning-based (MLbased) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At the same time, ML-based emulators of existing physically based parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art general circulation model (GCM) are robust with respect to the substantial structural and parametric change in the hostmodel: when used in two 7-month-long experiments with a new GCM, they remain stable and generate realisti
emulation; general circulation model; neural network; radiative transfer parameterization