التفاصيل البيبلوغرافية
| العنوان: |
Ionospheric Irregularities Reconstruction Using Multi-Source Data Fusion via Deep Learning. |
| المؤلفون: |
Penghao Tian, Bingkun Yu, Hailun Ye, Xianghui Xue, Jianfei Wu, Tingdi Chen |
| المصدر: |
Atmospheric Chemistry & Physics Discussions; 6/19/2023, p1-26, 26p |
| مستخلص: |
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 processing 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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
