ED‐Autoformer: A New Model for Precise Global TEC Forecast - Jiawei Zhou; Hongtao Cai; Xu Yan; Hong‐wen Xu; Kun Hu; Chao Xiong (Space Weather, 2025)
Abstract Total electron content (TEC) is a key parameter for characterizing ionospheric morphology and significantly impacts the Global Navigation Satellite System. The ionosphere responds dramatically to solar and geomagnetic activity, leading to substantial TEC fluctuations and disturbances. To improve TEC prediction accuracy, we propose Encoder‐Decoder (ED)‐Autoformer, a novel model combining ED structure with the Autoformer model. The model integrates time series data with TEC to enable 24‐hr forecasts of both disturbance storm time index (Dst) and TEC. Evaluated on global ionospheric maps TEC, our model achieves a 12.0% improvement (0.51 TECu) in the root mean squared error (RMSE) during solar maximum and an 8.9% improvement (0.14 TECu) in RMSE during solar minimum compared to the Convolutional Long‐Short‐Term Memory (ConvLSTM) method. Furthermore, ED‐Autoformer shows superior computational efficiency with 45.4% faster inference speed compared to ED‐ConvLSTM. We further analyzed TEC disturbances during geomagnetic storm periods. For the geomagnetic storm on 20 September 2015, the RMSE remained below 3.50 TECu for most periods, peaking at 5.50 TECu during the main phase. These results demonstrate the robustness of the model in accurately predicting TEC disturbances during geomagnetic storm periods.