Storm-Time Characteristics of Ionospheric Model (MSAP) Based on Multi-Algorithm Fusion

Geomagnetic storms induce ionospheric disturbances, affecting short-wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short-wave radio environment of the ionosphere. We use the Multi-Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi-LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI-2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI-2016 models. Total electron content (TEC) is an important parameter to describe the ionosphere. The prediction of TEC is crucial for short-wave communication systems. Geomagnetic storms disturb the ionosphere, thereby affecting the prediction of TEC. In this paper, the existing multi-step auxiliary prediction model is used to predict the TEC during geomagnetic storms. Using the International Reference Ionosphere-2016 model as the reference, the storm-time performance and time-varying characteristics of Multi-Step Auxiliary Prediction (MSAP) are studied. It is found that the MSAP model captures variations with solar activity, month, and Universal Time during geomagnetic storms. The MSAP model can predict total electron content (TEC) with high accuracy and stabilityThe MSAP model perform differently in different phases of geomagnetic storms