ML Prediction of Global Ionospheric TEC Maps.

This paper applies the convolutional long short‐term memory (convLSTM)‐based machine learning models to forecast global ionospheric total electron content (TEC) maps with up to 24 hr of lead time at a 1‐hr interval. Four convLSTM‐based models were investigated, and the one that implements the L1 loss function and the residual prediction strategy demonstrates the best performance. The convLSTM models are trained and evaluated using Center for Orbit Determination in Europe (CODE) global TEC maps over a period of nearly seven years from 19 October 2014 to 21 July 2021. Results show that the best convLSTM model outperforms the 1‐day predicted global TEC products released by CODE analysis center (c1pg) and persistence models under various levels of solar and geomagnetic activities, except for a lead time beyond 8 hr during the storm time where the c1pg has slightly better performance. The convLSTM forecasting performance degrades as the lead time increases. Plain Language Summary: Reliable specification and prediction of ionospheric total electron content (TEC) are not only helpful for mitigating uncertainties in global navigation satellite system‐based position, navigation, and timing services, but also for timely warning of space weather activities. We apply convolutional long short‐term memory (convLSTM)‐based machine learning models to forecast global ionospheric TEC maps with up to 24 hr of lead time at a 1‐hr interval. Four convLSTM‐based models were investigated, and the one that implements the L1 loss function and residual prediction strategy demonstrates the best performance. Moreover, our developed convLSTM model shows competitive performance when compared to two conventional models under various levels of solar and geomagnetic activities. Key Points: Four convolutional long short‐term memory (convLSTM)‐based models are investigated to forecast global ionospheric total electron content maps with up to 24 hr of lead time at a 1‐hr intervalThe one that implements the L1 loss function and residual strategy demonstrates the best performance among four convLSTM‐based modelsThis best performing convLSTM model also shows more accurate prediction compared to c1pg and persistence models [ABSTRACT FROM AUTHOR]