Articles | Volume 37, issue 1
Regular paper
01 Feb 2019
Regular paper |  | 01 Feb 2019

An improved pixel-based water vapor tomography model

Yibin Yao, Linyang Xin, and Qingzhi Zhao

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Cited articles

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Baltink, H. K., Marel, H. V. D., and Der Hoeven, A. V.: Integrated atmospheric water vapor estimates from a regional GPS network, J. Geophys. Res.-Atmos., 107, ACL 3-1–ACL 3-8, 2002. 
Bender, M., Stosius, R., Zus, F., Dick, G., Wickert, J., and Raabe, A.: GNSS water vapour tomography – Expected improvements by combining GPS, GLONASS and Galileo observations, Adv. Space Res., 47, 886–897, 2011. 
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Short summary
In this paper, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions by adaptive training for water vapor retrieval. Under different scenarios, tomography results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.