LARRES: A New Deep Learning Based Global Ionosphere Map Prediction Model With Large Receptive Field

Ionospheric delay poses significant challenges for satellite‐based communication and navigation applications. Predicting the global ionosphere variation can help to capture the vertical total electron content (VTEC) in advance, which improves the signal stability of communication and navigation, and supports these space‐related technologies. In existing studies, the ConvLSTM‐based method has achieved impressive results in global ionosphere map (GIM) prediction and gained increasing attention. However, the ConvLSTM model can only consider local features in GIM prediction since the single layer Convolutional Neural Network structure in ConvLSTM can not capture the global features. Inspired by the ResNet and the SimVP models, this paper proposes a new TEC prediction method based on a large receptive field model named large receptive field model with residual structure (LARRES). This method achieves a large receptive field effect through a deep global spatio‐temporal convolutional network, which allows to capture the global TEC features during the prediction process. The prediction performance of the LARRES model was evaluated with 8 years GIM data in both high and low solar activity years. The experimental results show that the proposed model outperforms the C1PG model from Center for Orbit Determination in Europe and two ConvLSTM‐based models. Compared to the C1PG model, the LARRES method can reduce RMS error by 28.7% and 10.4% during high and low solar activity years, respectively. The LARRES method can further reduce the global TEC prediction error by 0.35 TECU and 0.08 TECU subject to the ConvLSTM‐based models during high and low solar activity years respectively.