Research on Global Ionospheric TEC Deep Learning Forecasting Based on Spectral Whitening Method Index Group

The spatiotemporal evolution of global ionospheric Total Electron Content (TEC) directly affects the positioning accuracy of Global Navigation Satellite Systems (GNSS) and the reliability of communication systems. Accurate TEC prediction is therefore essential for space weather research and operations. Most existing ionospheric forecasting models use conventional solar activity and geomagnetic indices, such as F_10.7, Kp, and Dst, as external drivers. Their relatively low temporal resolution and infrequent updates make it difficult to represent rapid energy transfer and disturbance processes, which can constrain data-driven forecasts during extreme space weather events. To overcome this limitation, this study develops a physically driven index group using the Spectral Whitening Method (SWM). The SWM procedure suppresses periodic background components and emphasizes non-stationary disturbances. A high-latitude disturbance index is further incorporated so that the index group can characterize the chain of solar-terrestrial energy coupling from solar energy input to geomagnetic and ionospheric responses. The index group is evaluated within a two-stage deep learning forecasting framework. In the first stage, a deep neural network reconstructs three-dimensional electron density; in the second stage, a 3D Swin Transformer learns the spatiotemporal evolution and produces global TEC forecasts. The SWM-driven model is compared with the same framework driven by conventional indices and with the IRI-2020 empirical model. Results show that the SWM indices retain high statistical correlations with conventional indices while providing higher-frequency physical information. These properties accelerate model convergence and improve training stability. The SWM-driven model achieves better forecasting performance than IRI-2020 and exhibits stronger error-control capability under disturbed conditions. It also reduces the accumulation of errors in longer lead-time forecasts, indicating that the SWM index group supplies useful real-time physical constraints for deep learning. The findings demonstrate that jointly representing solar, geomagnetic, and high-latitude disturbance information can improve the responsiveness and robustness of global ionospheric forecasting. With its rapid updating capability and sensitivity to transient disturbances, the proposed index group provides a practical technical pathway toward high-performance and operational global space weather prediction.