Preprints
https://doi.org/10.5194/amt-2021-113
https://doi.org/10.5194/amt-2021-113

  25 May 2021

25 May 2021

Review status: this preprint is currently under review for the journal AMT.

A New ZHD Model for Real-Time Retrievals of GNSS-PWV

Longjiang Li1,2, Suqin Wu1,2, Kefei Zhang1,2,3, Xiaoming Wang4, Wang Li1,2, Zhen Shen1,2, Dantong Zhu1,2, Qimin He1,2, and Moufeng Wan1,2 Longjiang Li et al.
  • 1Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
  • 2School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116 China
  • 3Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, RMIT University, Melbourne VIC 3001, Australia
  • 4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 610209, China

Abstract. The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure over the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the Global Pressure and Temperature (GPT) models, are commonly used to determine the pressures for these GNSS stations. Due to the limited accuracies of the GPT models, the ZHD obtained from the model-derived pressure value is also of low accuracy, especially in mid- and high-latitude regions. To address this issue, a new ZHD model, named as GZHD, was investigated for real-time retrieval of PWV from GNSS in this study. The ratio of the ZHD to the zenith total delay (ZTD) was first calculated using sounding data from 505 globally distributed radiosonde stations selected from the stations that had over 5,000 samples. It was found that the temporal variation in the ratio was dominated by the annual and semiannual components, and the amplitude of the annual variation was dependent upon the geographical location of the station. Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The 20-year (2000–2019) radiosonde data at 558 global stations and the 9-year (2006–2014) COSMIC-1 data, which were also globally distributed, were used as the training samples of the new model. The GZHD model was evaluated using two sets of references: the integrated ZHD obtained from sounding data over 137 radiosonde stations and ERA5 reanalysis data. The performance of the new model was also compared with GPT3. Results showed the new model outperformed GPT3, especially in mid- and high-latitude regions. When radiosonde-derived ZHD was used as the reference, the accuracy, which was measured by the root mean square error (RMSE) of the samples, of the GZHD-derived ZHD, was 22 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was 35 % better than GPT3-derived ZHD. In addition, the PWV derived from 93 GNSS stations resulting from GZHD-derived ZHD was also evaluated and the result indicated that the accuracy of the PWV was improved by 23 %.

Longjiang Li et al.

Status: open (until 22 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-113', Anonymous Referee #1, 19 Jun 2021 reply

Longjiang Li et al.

Longjiang Li et al.

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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.