Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

Abstract Efficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we...

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Principais autores:	Meiling Cheng, Fangxin Fang, Ionel M. Navon, Jie Zheng, Xiao Tang, Jiang Zhu, Christopher Pain
格式:	Artigo
語言:	Inglês
出版:	American Geophysical Union (AGU) 2022-03-01
叢編:	Journal of Advances in Modeling Earth Systems
主題:	ozone forecasting spatio‐temporal generative adversarial network variational autoencoder deep learning
在線閱讀:	https://doi.org/10.1029/2021MS002806
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