A Global TEC Map Forecasting Method Based on Periodic-Matched Residual Prediction and Longitude-Circular Boundary-Aware Convolution

Total Electron Content (TEC) is a key parameter for characterizing the state of the ionosphere, and its spatiotemporal variations can significantly affect satellite navigation, radio communication, and space weather monitoring. To address the pronounced diurnal periodicity in global TEC map forecasting and the commonly neglected continuity at longitudinal boundaries, this study proposes an encoder–decoder ConvLSTM model that integrates periodic-matched residual prediction with longitude-circular boundary-aware convolution, namely the Longitude-Circular Periodic-Residual ED-ConvLSTM (LC-PR-EDConvLSTM). In the proposed model, the TEC map at the same temporal phase on the previous day is used as a periodic background field, enabling the network to focus on learning the residual variation in future TEC relative to this background. Meanwhile, longitude-circular padding is introduced into the convolution operations to preserve the spatial continuity of global TEC maps across the −180° and 180° meridians. Experiments were conducted using CODE global ionospheric map products from 2009 to 2019, with 12 TEC maps from the previous day used as inputs to predict 12 TEC maps for the following day. The results show that LC-PR-EDConvLSTM achieves RMSE values of 3.68 TECU and 1.37 TECU on the 2015 high-solar-activity test set and the 2019 low-solar-activity test set, respectively, outperforming the C1pg, ED-ConvGRU, and ED-ConvLSTM benchmark models. Ablation experiments further verify the effectiveness of the periodic-matched residual prediction strategy and the longitude-circular boundary-aware convolution. Analyses of typical space weather events and latitudinal regions demonstrate that the proposed model provides stable forecasting performance under complex space weather conditions and across most latitude regions.