LAUSR

Monitoring of critical frequency variation in the ionospheric F2 layer (foF2) has lately received considerable attention for the frequency selection in skywave communication. Currently, both the deep learning and machine learning model have made a striking accomplishment in comprehending the ionosphere. In this letter, we utilize an Informer architecture to predict the foF2 parameter under the two scenarios in terms of the quiet space weather and storm events. The Informer method applied the past and present foF2 samples to capture time sequence processing characteristics, trained and tested for 2017–2018 years’ measurement samples at Beijing, China (40.3°N, 116.2°E). It is evident from the results that the Informer performed better than International Reference Ionosphere 2016, Elman network, long short-term memory (LSTM), and bidirectional LSTM models. The Informer models extensively captured the correlation within the foF2 sequence features and better predicted it in storm events.