Articles | Volume 34, issue 1
https://doi.org/10.5194/angeo-34-143-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-34-143-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Regular paper
| 
28 Jan 2016
Regular paper |
| 28 Jan 2016
A method to improve the utilization of GNSS observation for water vapor tomography
Y. B. Yao
CORRESPONDING AUTHOR
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Key Laboratory of Geospace Environment and Geodesy, Ministry of
Education, Wuhan University, Wuhan, China
Collaborative Innovation Center for Geospatial Technology, Wuhan,
China
Q. Z. Zhao
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
B. Zhang
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
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Cited
30 citations as recorded by crossref.
- An improved GNSS tropospheric tomographic model with an extended region and combining virtual signals S. Liu et al. https://doi.org/10.1016/j.atmosres.2023.106703
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- Regional modeling and forecasting of precipitable water vapor using least square support vector regression S. Ghaffari-Razin et al. https://doi.org/10.1016/j.asr.2023.01.030
- Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach Q. Zhao et al. https://doi.org/10.3390/rs10081241
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- Maximally Using GPS Observation for Water Vapor Tomography Y. Yao & Q. Zhao https://doi.org/10.1109/TGRS.2016.2597241
- Optimized Approach for Near-Real-Time 3-D Water Vapor Estimation Technique Using the Informer Model in GNSS Y. Zhu et al. https://doi.org/10.1109/TGRS.2024.3495680
- Preliminary result of capturing the signature of heavy rainfall events using the 2-d-/4-d water vapour information derived from GNSS measurement in Hong Kong Q. Zhao et al. https://doi.org/10.1016/j.asr.2020.06.013
- Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique W. Zhang et al. https://doi.org/10.1007/s00190-020-01386-4
- Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset Z. Adavi et al. https://doi.org/10.1109/JSTARS.2020.3027909
- An improved pixel-based water vapor tomography model Y. Yao et al. https://doi.org/10.5194/angeo-37-89-2019
- A modified three-dimensional ionospheric tomography algorithm with side rays Y. Yao et al. https://doi.org/10.1007/s10291-018-0772-4
- Influence of station density and multi-constellation GNSS observations on troposphere tomography Q. Zhao et al. https://doi.org/10.5194/angeo-37-15-2019
- A troposphere tomography method considering the weighting of input information Q. Zhao et al. https://doi.org/10.5194/angeo-35-1327-2017
- A new method for tropospheric tomography using GNSS and Fengyun-4A data M. Zhang et al. https://doi.org/10.1016/j.atmosres.2022.106460
- An Investigation of Near Real-Time Water Vapor Tomography Modeling Using Multi-Source Data L. Tong et al. https://doi.org/10.3390/atmos13050752
- Hybrid Regularized GPS Tropospheric Sensing Using 3-D Ray Tracing Technique S. Haji-Aghajany & Y. Amerian https://doi.org/10.1109/LGRS.2018.2853183
- Tomographic Reconstruction of Water Vapor Density Fields From the Integration of GNSS Observations and Fengyun-4A Products B. Chen et al. https://doi.org/10.1109/TGRS.2023.3239392
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- An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. https://doi.org/10.1109/JSTARS.2023.3278302
- A new troposphere tomography algorithm with a truncation factor model (TFM) for GNSS networks Q. Zhao et al. https://doi.org/10.1007/s10291-019-0855-x
- A new approach for GNSS tomography from a few GNSS stations N. Ding et al. https://doi.org/10.5194/amt-11-3511-2018
- An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area Q. Zhao & Y. Yao https://doi.org/10.5194/angeo-35-87-2017
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- An optimal tropospheric tomography approach with the support of an auxiliary area Q. Zhao et al. https://doi.org/10.5194/angeo-36-1037-2018
- Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization L. Yang et al. https://doi.org/10.1155/2021/6415589
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Latest update: 12 Jun 2026
Short summary
Existing water vapor tomographic methods use Global Navigation Satellite System (GNSS) signals penetrating the entire research area while they do not consider signals passing through its sides. To solve this issue, an approach which uses GPS data with both signals that pass the side and top of a research area is proposed. The advantages of proposed approach include improving the utilization of existing GNSS observations and increasing the number of voxels crossed by satellite signals.
Existing water vapor tomographic methods use Global Navigation Satellite System (GNSS) signals...
