DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite
Siwei Li, Jia Xing
Abstract
<p>Accurate measurement of atmospheric chemicals is essential for understanding their impact on human health, climate, and ecosystems. Satellites provide a unique advantage by capturing data across the entire atmosphere, but their measurements often lack vertical details. Here, we introduce DeepSAT4D, an innovative method that efficiently reconstructs 4D chemical concentrations from satellite data. It achieves this by regenerating the dynamic evolution of vertical structure, intricately linked to complex atmospheric processes such as plume rise and transport, using advanced deep learning techniques. Its application with the Ozone Monitoring Instrument - Nitrogen Dioxide, a commonly used satellite product, demonstrates good agreement with ground-based monitoring sites in China from 2017 to 2021. Additionally, DeepSAT4D successfully captures emission reductions during 2020-pandemic shutdown. These findings emphasize DeepSAT4D��s potential to enhance our understanding of the complete atmospheric chemical composition and to provide improved assessments of its impact on human health and Earth��s ecosystem in the future.</p>