ISNet: Decomposed Dynamic Spatio‐Temporal Neural Network for Ionospheric Scintillation Forecasts - Zhixu Gao; Yanhong Chen; Xianzhi Ao; Fulu Yue; Hong Chen; Hao Deng; Bingxian Luo; Xin Wang; Tianjiao Yuan (Space Weather, 2025)
Abstract Accurate prediction of ionospheric scintillation is essential for ensuring the reliability of spaceborne and ground‐based radio wave technology infrastructures, including but not limited to navigation and communication systems. In this study, we propose a deep learning‐based Ionospheric Scintillation Network (ISNet), which can predict the regional scintillation index S4 1 hour in advance. The novel ISNet decomposes the S4 index into background and disturbance fields and treats them separately. The model also employs specialized modules to capture the time‐delay effect between external disturbances and the associated scintillation. A flexible dynamic data reconstruction strategy is adopted, which allows the model to learn directly from high‐fidelity scintillation observations. Our results show that ISNet can provide accurate regional ionospheric scintillation forecasts in the low‐latitude regions of China. The RMSEs for weak, moderate, and strong scintillations in the test set are 0.053, 0.124, and 0.183, respectively. Compared with 12 other methods, ISNet displays a significant advantage in predicting moderate‐to‐strong scintillations. We also quantified the relative importance of input features to model predictions through the interpretable technique, which may provide valuable insights into model interpretation and feature selection.