Exploring Pathways to More Accurate Machine Learning Emulation of Atmospheric Radiative Transfer

Machine learning (ML) parameterizations of subgrid physics is a growing research area. A key question is whether traditional ML methods such as feed-forward neural networks (FNNs) are better suited for representing only specific processes. Radiation schemes are an interesting example, because they c...

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Bibliographic Details
Main Author:	Ukkonen, P. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2022
Subjects:	artificial neural network atmosphere atmospheric radiative transfer Atmospheric radiative transfer climate modeling Feed forward neural net works Feedforward neural networks heating machine learning Machine learning Machine learning methods Machine-learning Mean square error neural networks Neural-networks physics Program processors Radiative flow radiative transfer Radiative transfer Recurrent NN Research areas shortwave radiation Sub-grids weather and climate modeling Weather and climate models
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Exploring Pathways to More Accurate Machine Learning Emulation of Atmospheric Radiative Transfer

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