A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrieval

Xie, Shaofeng; Zhang, Jihong; Huang, Liangke; Chen, Fade; Wu, Yongfeng; Wang, Yijie; Liu, Lilong

doi:https://doi.org/10.5194/gmd-18-6987-2025

Articles | Volume 18, issue 19

https://doi.org/10.5194/gmd-18-6987-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-6987-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 19

Development and technical paper

|

10 Oct 2025

Development and technical paper |

| 10 Oct 2025

A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrieval

Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu

Data sets

A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying lapse rate in GNSS precipitable water vapor retrieval S. Xie et al. https://doi.org/10.5281/zenodo.11258940

Short summary

We developed a new global atmospheric weighted mean temperature (T_m) model considering time-varying vertical adjustment rate. Firstly, a global T_m vertical adjustment rate model (NGGTm-H) was developed using the sliding-window algorithm. Secondly, the daily variation characteristics of T_m and its relationships with geographical situations were investigated. Finally, a hybrid-grid global T_m model considering the time-varying vertical adjustment rate (NGGTm) was developed.