Prediction of Global Ionospheric TEC Based on Deep Learning.

The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi‐step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS‐TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time‐shift algorithm of IGS‐TEC. The result suggests that the Multi‐step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time. Plain Language Summary: The prediction of global Total Electron Content map is very important for the accuracy of global navigation satellite systems based global positioning system, satellite communications and other space communications applications. As is well known to all, the prediction accuracy usually decrease significantly with the increasing of the prediction time. In order to solve this issue, four different long short‐term memory‐based algorithms are tested to explore a direction that can effectively alleviate the increasing error with prediction time. The result shows that Multi‐step auxiliary prediction model can effectively alleviate the increasing error with prediction time. Moreover, it has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm. Key Points: Multi‐step auxiliary predictionmodel can effectively alleviate the increasing error with prediction timeMulti‐step auxiliary prediction model can predict international global navigation satellite systems service‐total electron content (TEC) map of next 6 days, and the mean absolute deviation and root mean square error are 2.485 and 3.511 TECU, respectivelyMulti‐step auxiliary prediction model has a good generalization performance and low error during a geomagnetic storm [ABSTRACT FROM AUTHOR]