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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書誌詳細

出版年:	Journal of Advances in Modeling Earth Systems
主要な著者:	Meiling Cheng, Fangxin Fang, Ionel M. Navon, Jie Zheng, Xiao Tang, Jiang Zhu, Christopher Pain
フォーマット:	論文
言語:	英語
出版事項:	American Geophysical Union (AGU) 2022-03-01
主題:	ozone forecasting spatio‐temporal generative adversarial network variational autoencoder deep learning
オンライン･アクセス:	https://doi.org/10.1029/2021MS002806