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

Li, Longjiang; Wu, Suqin; Zhang, Kefei; Wang, Xiaoming; Li, Wang; Shen, Zhen; Zhu, Dantong; He, Qimin; Wan, Moufeng

doi:https://doi.org/10.5194/amt-14-6379-2021

Articles | Volume 14, issue 10

Atmos. Meas. Tech., 14, 6379–6394, 2021

https://doi.org/10.5194/amt-14-6379-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Atmos. Meas. Tech., 14, 6379–6394, 2021

https://doi.org/10.5194/amt-14-6379-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 10

Research article 01 Oct 2021

Research article | 01 Oct 2021

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

Longjiang Li et al.

Data sets

COSMIC RO data University Corporation for Atmospheric Research (UCAR) https://data.cosmic.ucar.edu/gnss-ro/cosmic1/repro2013/

ERA5 hourly data on pressure levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

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.