Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-11517-2025
https://doi.org/10.5194/acp-25-11517-2025
Research article
 | 
29 Sep 2025
Research article |  | 29 Sep 2025

Global ionospheric sporadic E intensity prediction from GNSS RO using a novel stacking machine learning method incorporated with physical observations

Tianyang Hu, Xiaohua Xu, Jia Luo, Jialiang Hou, and Haifeng Liu

Cited articles

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Asamoah, E. N., Cafaro, M., Epicoco, I., De Franceschi, G., and Cesaroni, C.: A stacked machine learning model for the vertical total electron content forecasting, Adv. Space Res., 74, 223–242, https://doi.org/10.1016/j.asr.2024.04.055, 2024. 
Batista, I. S. and Abdu, M. A.: Magnetic storm associated delayed sporadic E enhancements in the Brazilian Geomagnetic Anomaly, J. Geophys. Res., 82, https://doi.org/10.1029/JA082i029p04777, 1977. 
Bergsson, B. and Syndergaard, S.: Global Temporal and Spatial Variations of Ionospheric Sporadic-E Derived From Radio Occultation Measurements, J. Geophys. Res.-Space, 127, e2022JA030296, https://doi.org/10.1029/2022JA030296, 2022. 
Chu, Y. H., Wang, C. Y., Wu, K. H., Chen, K. T., Tzeng, K. J., Su, C. L., Feng, W., and Plane, J. M. C.: Morphology of sporadic E layer retrieved from COSMIC GPS radio occultation measurements: Wind shear theory examination, J. Geophys. Res.-Space, 119, 2117–2136, https://doi.org/10.1002/2013JA019437, 2014. 
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Short summary
Sporadic E (Es) layers are an irregularity in ionospheric E region. Their formation is related to multiple atmospheric physical and chemical processes. Accurate Es intensity prediction is significant for understanding atmospheric coupling. We proposed a novel stacking machine learning method incorporating physical observations to achieve higher-precision global Es intensity prediction than previous methods. Our results indicate the importance of considering related physical factors for Es prediction. 
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