A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV

• Main Authors: L. Li, S. Wu, K. Zhang, X. Wang, W. Li, Z. Shen, D. Zhu, Q. He, M. Wan
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-10-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://amt.copernicus.org/articles/14/6379/2021/amt-14-6379-2021.pdf

<p>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 at 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 GZHD, was investigated for real-time retrieval of GNSS-PWV 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 5000 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 (Constellation Observing System for Meteorology, Ionosphere, and Climate) 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 and ERA5 reanalysis data. The performance of the new model was also compared with GPT3, the latest version. 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 about 21 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was about 30 % better than GPT3-derived ZHD. In addition, the real-time PWV derived from 41 GNSS stations resulting from GZHD-derived ZHD was also evaluated, and the result indicated that the accuracy of the PWV was improved by 21 %.</p>