STARNet: A Deep-Learning Algorithm for Surface Shortwave Radiation Retrieval From Fengyun-4A

Abstract

Satellite-retrieved surface shortwave radiation is indispensable to solar energy meteorology applications. In stark contrast to conventional irradiance retrieval algorithms that are confined to individual pixel information, this work proposes the STARNet (Spatio-Temporal Association-based Retrieval Network), which is a deep-learning algorithm that exploits the information embedded in the spatio-temporal neighbors of a target pixel. The algorithm holds three technical innovations: (a) a data preprocessing method that highlights the correlation- and causality-type climatology associations in the original reflectance and brightness temperature observations; (b) a graph network cascade that extracts topological spatio-temporal features, and (c) a multi-scale convolution network that extracts regular spatio-temporal features. The empirical part of this work showcases irradiance retrieval from Fengyun-4A over China. True out-of-sample verification demonstrates that STARNet can outperform physical and conventional data-driven retrieval algorithms. Most importantly, STARNet is exceedingly general and thus applicable to many other retrieval tasks, such as those for aerosols or clouds.