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.
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.
the Creative Commons Attribution 4.0 License.
Global ionospheric sporadic E intensity prediction from GNSS RO using a novel stacking machine learning method incorporated with physical observations
Tianyang Hu
School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China
Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan, 430079, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China
Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan, 430079, China
Jialiang Hou
School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China
Haifeng Liu
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
As a fundamental type of atmospheric wave, the gravity wave carries energy whose intensity is characterized by its momentum flux. We proposed a new method for extracting gravity wave momentum flux magnitude based on satellite radio occultation temperature profiles, which extends the multi-wave idea from profile-pair framework to profile-triple framework. Our method significantly improves data utilization rate and enables a more complete measurement of total gravity wave momentum flux magnitude.
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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.
Sporadic E (Es) layers are an irregularity in ionospheric E region. Their formation is related...
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