A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network

The total electron content (TEC) is an important parameter for characterizing the morphology of the ionosphere. Modeling the ionospheric TEC accurately during the storm time could contribute to the operation of global navigation satellite systems (GNSS), satellite communications, and other applications. This study uses an image-based convolutional long short-term memory (ConvLSTM) network with multichannel features to forecast ionospheric TEC during the quiet periods and storm periods. The sunspot number (SSN), solar wind velocity (V-sw), Dst, and Kp geomagnetic indices are firstly fed into the model as the channel features to improve generalization performance. Based on the variation of the Dst index, we have collected gridded TEC maps from 2011 to 2018 with a 1-h interval from the global ionospheric maps (GIM) as the data set including quiet periods and storm periods of ionospheric TEC. The performance of the ConvLSTM model in forecasting TEC is also compared with other deep learning models such as LSTM, gated recurrent unit (GRU), and LSTM-CNN. Furthermore, the accuracy consistency of the ConvLSTM model during the different phases of the storm period is also evaluated for the different output steps of predicted TEC maps. The optimal combination of input features for the model is also investigated during the storm period. Testing results show that the ConvLSTM network with multichannel features has good prediction performance for quiet periods and storm periods by incorporating both solar and geomagnetic activity indices. The statistical indicators show that the ConvLSTM model performs well with lower mean absolute error (MAE), root mean square error (RMSE), and larger correlation coefficient (R) compared with other methods. We have demonstrated that the model with a larger prediction step has worse prediction performance at the low-latitude area, especially during the storm period. In our future work, the larger TEC data set and more solar and geomagnetic indices will be investigated.