Real‐Time Regional Ionosphere Modeling With RFR‐Net Over China

Abstract The mid‐to‐low‐latitude ionosphere, influenced by phenomena such as the Equatorial Ionization Anomaly, responds more sensitively to changes in solar activity, which negatively affect the transmission of various electromagnetic signals. Moreover, next‐generation technologies, particularly Precise Point Positioning‐Real‐Time Kinematic (PPP‐RTK), require more instant and detailed information on near‐earth space environments. However, current ionospheric Total Electron Content (TEC) maps are often post‐processed and designed for global applications. Under this challenge, we develop a real‐time, high‐precision regional ionospheric TEC map service using a deep learning inpainting Recurrent Feature Reasoning (RFR) method. Given the limited ionospheric observation resources, our approach significantly reduces the scale of observational data by utilizing only 2.5% of the total TEC data. This is achieved through the RFR and the Knowledge Consistent Attention (KCA) module embedded in the RFR‐TEC model, where the RFR module leverages pixel correlations for robust estimation, and the KCA mechanism enforces patch consistency. Results indicate that the real‐time RFR‐TEC achieves TEC accuracy comparable to the post‐processed CODE‐TEC and surpasses the real‐time UPC‐TEC by 47.8% in long‐term validation. Additionally, the RFR‐TEC map demonstrates superior stability compared to the real‐time UPC‐TEC, while its performance varies with the seasons.