Developing an Explainable Variational Autoencoder (VAE) Framework for Accurate Representation of Local Circulation in Taiwan

This study develops an explainable variational autoencoder (VAE) framework to efficiently generate high‐fidelity local circulation patterns in Taiwan, ensuring an accurate representation of the physical relationship between generated local circulation and upstream synoptic flow regimes. Large ensemb...

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Vydané v:	Journal of geophysical research. Atmospheres Ročník 129; číslo 12
Hlavní autori:	Hsieh, Min‐Ken, Wu, Chien‐Ming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington Blackwell Publishing Ltd 28.06.2024
Predmet:	Accuracy Circulation Circulation patterns Climate and weather Climate change Climate change scenarios Climate prediction Climatic conditions deep generative model deep learning explainable artificial intelligence Flow distribution Flow pattern Fluid flow large eddy simulation local circulation Local flow Neural networks Representations Simulation Synoptic weather conditions Training Uncertainty Vortices Weather Weather conditions Weather patterns Wind speed Taiwan
ISSN:	2169-897X, 2169-8996
On-line prístup:	Získať plný text
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