Deep Learning Insights Into Ionospheric Sporadic E Irregularities Under Different Solar Activity Conditions

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 4
• Main Authors: Tian, Penghao, Yu, Bingkun, Ye, Hailun, Xue, Xianghui, Wu, Jianfei, Chen, Tingdi
• Format: Journal Article
• Language: English
• Published: Wiley 01.12.2024
• Subjects: deep learning; ionospheric irregularities; neural networks; sporadic E layer
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000279

Solar activity profoundly modulates many atmospheric coupling systems, particularly the morphology of the ionospheric irregularities, which is crucial for reliable radio communication. However, the understanding of its long‐term response behavior under different solar activity conditions remains limited, and the exact evolutionary mechanisms of global scale ionospheric coupling system remain poorly constrained. Here we initially show from 2007 to 2018, the weak ionospheric sporadic E scintillation in high latitude regions experienced intensification corresponding with periods of high solar activity. Subsequently, we extend a deep learning model framework called SELF‐ANN proposed by Tian to further clarify the underlying relationship between solar activity and ionospheric irregularities. Compared to SELF‐ANN, we expand the scope of analysis to include data spanning from 2007 to 2018, thus broadening the investigation into the impacts of solar variability. Using long‐term solar activity data and observations of the ionospheric E region irregularity from COSMIC RO, this model was trained to provide solar‐dependent ionospheric morphology. Based on the model, we have successfully achieved high‐precision modeling of weak ionospheric E region irregularities, which commonly occur and are challenging to detect with ionosondes, under different solar activity conditions. Quantitatively, the model achieves a mean absolute error of 0.004, coupled with a Spearman's R value of 0.589 for weak scintillation. Ionospheric reconstructions under different solar activity conditions can improve the understanding of ionospheric evolutionary mechanisms and underscore the importance of incorporating solar variations into ionosphere changes forecasting to ensure the development of future reliable communications. Plain Language Summary In this study, we explored how the sun's activity affects the Earth's ionosphere, a layer of the atmosphere that's crucial for sending radio signals over long distances. The ionosphere can have “irregularities” that disrupt these signals, which tend to change with solar activity. Our study focused on understanding how these irregularities in the ionosphere change with different levels of solar activity. From 2007 to 2018, we noticed that a specific kind of disturbance in the ionosphere, called sporadic E, becomes more frequent in the weak value range at high latitudes when the sun is more active. To understand this better, we used artificial intelligence approach to predict and analyze these changes. We trained this model with extensive data about the sun's activity and the ionosphere's behavior, helping us predict these disturbances more accurately. Our results show that the model can successfully predict these ionospheric changes, especially during periods of high solar activity. It help us better predict and prepare for communication challenges, ensuring that our radio and satellite communications stay reliable in the future, regardless of how active the sun is. Key Points The incidence of weak ionospheric scintillation intensifies at high latitudes during periods of high solar activity A deep‐learning based model was proposed to extract the latent relationship between the solar activities and ionospheric irregularities The model demonstrates both high accuracy and efficacy in reconstructing global ionospheric irregularities during different solar activities