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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Veröffentlicht in:	arXiv.org
Hauptverfasser:	Behrens, Gunnar, Beucler, Tom, Gentine, Pierre, Iglesias-Suarez, Fernando, Pritchard, Michael, Eyring, Veronika
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 26.07.2022
Schlagworte:	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-Zugang:	Volltext
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