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

Abstract

Abstract 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.