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...

Celý popis

Podrobná bibliografia

Vydané v:	arXiv.org
Hlavní autori:	Behrens, Gunnar, Beucler, Tom, Gentine, Pierre, Iglesias-Suarez, Fernando, Pritchard, Michael, Eyring, Veronika
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 26.07.2022
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!