Ionospheric Irregularities Reconstruction Using Multi-Source Data Fusion via Deep Learning

Abstract. The ionospheric sporadic E (Es) layer is the intense plasma irregularities between 80 and 130 km in altitude, which is generally unpredictable. Reconstructing the morphology of sporadic E layer is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but also useful to solve a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including high-resolution ERA5 reanalysis dataset, COSMIC RO measurements, and integrated data from OMNI. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary pro- cessing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can provide a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood.