Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1487-2025
© 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/gmd-18-1487-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Mar 2025
Development and technical paper |
| 10 Mar 2025
| 10 Mar 2025
Empirical modeling of tropospheric delays with uncertainty
Jungang Wang
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Institut für Geodäsie und Geoinformationstechnik, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany
Space Geodetic Techniques, GeoForschungsZentrum (GFZ), Potsdam 14473, Germany
Junping Chen
CORRESPONDING AUTHOR
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
Shanghai Key Laboratory of Space Navigation and Positioning Techniques, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Yize Zhang
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Related authors
Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Duojie Weng, Tong Liu, Xiaolong Mi, Benedikt Soja, and Lei Bai
EGUsphere, https://doi.org/10.22541/essoar.172081357.72805131/v3, https://doi.org/10.22541/essoar.172081357.72805131/v3, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Satellite navigation can help measure water vapor, but most stations lack weather sensors. This limits accurate real-time estimates. We developed a method using artificial intelligence weather models to calculate two key atmospheric quantities locally and quickly anywhere. Tests with three models gave water-vapor errors below one millimeter for five-day forecasts, better than current methods. This can improve real-time weather monitoring and forecasts when more satellite data are used.
Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Duojie Weng, Tong Liu, Xiaolong Mi, Benedikt Soja, and Lei Bai
EGUsphere, https://doi.org/10.22541/essoar.172081357.72805131/v3, https://doi.org/10.22541/essoar.172081357.72805131/v3, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Satellite navigation can help measure water vapor, but most stations lack weather sensors. This limits accurate real-time estimates. We developed a method using artificial intelligence weather models to calculate two key atmospheric quantities locally and quickly anywhere. Tests with three models gave water-vapor errors below one millimeter for five-day forecasts, better than current methods. This can improve real-time weather monitoring and forecasts when more satellite data are used.
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Short summary
The Global Navigation Satellite System (GNSS) is widely used for real-time monitoring and early warning of geohazards. Accurate modeling of tropospheric delays is critical to achieving high-precision GNSS solutions, and using external delays can improve real-time GNSS convergence times. Current empirical delay models only provide delay but not uncertainty. We propose a global empirical delay model with uncertainty and demonstrate its benefits in accelerating GNSS positioning convergence.
The Global Navigation Satellite System (GNSS) is widely used for real-time monitoring and early...
