Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Behrens, Gunnar, Beucler, Tom, Gentine, Pierre, Iglesias-Suarez, Fernando, Pritchard, Michael, Eyring, Veronika
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 26.07.2022
Subjects:	Climate models Convection Cumulus clouds Deep learning Long wave radiation Machine learning Moisture content Moisture effects Neural networks Reduction Simulation Thermal energy
ISSN:	2331-8422
Online Access:	Get full text
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