Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-6379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV
Longjiang Li
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Suqin Wu
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Kefei Zhang
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, RMIT University, Melbourne, Victoria, 3001, Australia
Xiaoming Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 610209, China
Wang Li
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Zhen Shen
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Dantong Zhu
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Qimin He
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Moufeng Wan
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
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This work presents the most comprehensive global GNSS climate dataset, covering a 22-year period and over 5085 stations. The data enables the monitoring of atmospheric humidity and circulation, which is crucial for understanding the mechanisms of climate change and climate-related extreme events. Through rigorous quality control, assessment and comparison with trusted sources, the dataset supports better weather forecasting, long-term climate monitoring, and global climate risk assessment.
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GNSS signal is delayed when it transmits through the neutral gas. In this contribution, a new model was developed for reducing the VMF1/VMF3 grid-wise ground-surface ZHD and ZWD values to the target height to improve the ZHD and ZWD interpolation performance. Test results showed that the accuracy of the ZHD, ZWD interpolated from the VMF1/VMF3 products deduced by the new model was significantly improved compared to traditional methods.
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Atmos. Meas. Tech., 14, 2529–2542, https://doi.org/10.5194/amt-14-2529-2021, https://doi.org/10.5194/amt-14-2529-2021, 2021
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In GPS or Global navigation satellite systems (GNSS) meteorology, precipitable water vapor (PWV) at a station is obtained from a conversion of the GNSS signal zenith wet delay (ZWD) using a conversion factor which is a function of weighted mean temperature (Tm) over the site. We developed a new global grid-based empirical Tm model using ERA5 reanalysis data. The model-predicted Tm value has significance for applications needing real-time or near real-time PWV converted from GNSS signals.
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Short summary
The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in mid- and high-latitude regions. To address this issue, the ratio of the ZHD to zenith total delay (ZTD) is firstly investigated; then, based on the relationship between the ZHD and ZTD, a new ZHD model was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The model outperforms blind models.
The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in...