The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision

被引:0
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作者
Huang, Qiuying [1 ,2 ]
Wang, Xiaoming [1 ,3 ]
Li, Haobo [4 ]
Zhang, Jinglei [1 ]
Han, Zhaowei [1 ,2 ]
Liu, Dingyi [1 ,2 ]
Li, Yaping [1 ]
Zhang, Hongxin [1 ,2 ]
机构
[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing,100094, China
[2] School of Electronic, Electrical and Communicating Engineering, University of Chinese Academy of Sciences, Beijing,101408, China
[3] College of Resources and Environment, University of Chinese Academy of Sciences, Beijing,100049, China
[4] School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne,VIC,3001, Australia
基金
中国国家自然科学基金;
关键词
Atmospheric monitoring - Climate datasets - Global navigation satellite system - Global navigation satellite system atmospheric monitoring - Global Navigation Satellite Systems - Precipitable water vapor - Precipitable water vapour - Zenith total delay - Zenith total delays;
D O I
10.3390/rs16203885
中图分类号
学科分类号
摘要
Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. © 2024 by the authors.
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