Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 18
Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-11517-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-11517-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 18
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

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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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