Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction
Sporadic E (Es) 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 Es layers is crucial for ensuring the precision and dep...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press
2025-01-01
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| Series: | Earth and Planetary Physics |
| Subjects: | |
| Online Access: | http://www.eppcgs.org/article/doi/10.26464/epp2024048?pageType=en |
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| Summary: | Sporadic E (Es) 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 Es layers is crucial for ensuring the precision and dependability of navigation satellite systems. In this study, we present Es 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 Es layer occurrences and characteristics, in particular. |
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| ISSN: | 2096-3955 |