Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps

This study presents an image-based convolutional long short-term memory (convLSTM) machine learning algorithm to predict storm-time ionospheric irregularities. Unlike existing methods that are either focused on irregularities at individual locations or treat the irregularity prediction as a classification problem, the convLSTM-based architecture forecasts an entire regions' ionospheric irregularity occurrence and intensity values. We implemented the convLSTM-based model with a custom-designed loss function (convLSTM-Lc) that includes a dynamic penalty on the difference between the truth and the predicted rate of total electron content index (ROTI) maps. The convLSTM-Lc is trained with real ROTI data collected during January 1–August 7, 2015 from ∼550 global navigation satellite system receivers located in (45°–90°N, 0°–180°W). Test results show that the convLSTM-Lc algorithm can forecast irregularity structures more accurately than a convLSTM model that implements conventional loss functions. The model also outperforms the convLSTM-L1, convLSTM-L2, and persistence models according to statistical classification metrics with a lead time of up to 60 min.