RA‐ConvLSTM: Recurrent‐Architecture Attentional ConvLSTM Networks for Prediction of Global Total Electron Content

Abstract The ionosphere poses a significant source of error in satellite‐based navigation systems for aviation and radio communication applications. Accurate estimation of the total electron content (TEC) can effectively mitigate the impact of such errors. However, constrained by observational techniques, the acquisition of global ionospheric TEC in practical applications relies heavily on high‐precision forecasting products. In this study, we construct a global ionospheric forecasting model based on the global ionospheric TEC products disseminated by the International GNSS Service (IGS) and deep learning. We incorporate an attention module to extract global spatiotemporal features from historical ionospheric data and employ these features to predict the TEC values over the next 24 hr. Additionally, we select a long short‐term memory (LSTM) model and a ConvLSTM model as baseline models for comparison, conducting experiments under varying solar activity conditions. The experimental results demonstrate that RA‐ConvLSTM model outperforms the other two models in quantifying the performance of the models. During high solar activity years, the bias and Root Mean Square Error (RMSE) of RA‐ConvLSTM model are −0.0298 TECU and 3.8980 TECU, respectively, while during low solar activity years, these values are 0.0905 TECU and 1.5059 TECU, marking a notable improvement over the comparative models. Furthermore, by contrasting the precision of the three forecasting models during geomagnetic storms, the RA‐ConvLSTM model exhibits the least fluctuations in accuracy, indicative of a higher degree of stability in its forecasting outcomes.