Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E (E_s) layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90–130 km. Because they can significantly influence radio communications and navigation systems, accurate forecasting of E_s layers is crucial for ensuring the precision and dependability of navigation satellite systems. In this study, we present E_s predictions made by an empirical model and by a deep learning model, and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations. The deep learning model exhibited significantly better performance, as indicated by its high coefficient of correlation (r = 0.87) with RO observations and predictions, than did the empirical model (r = 0.53). This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally, and into predicting E_s layer occurrences and characteristics, in particular.