Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

Abstract An accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters W prot (a measure of the ionospheric disturbance during storm time) and E conv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid