EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dynamic Tomography Principle: An Adaptive Variable-Scale Approach to GNSS Atmospheric Water Vapor Tomography

Wenyuan Zhang1,2, Gregor Moeller1, Shubi Zhang2, Nanshan Zheng2, and Nan Ding3
Wenyuan Zhang et al.
  • 1Institute of Geodesy and Photogrammetry, ETH Zürich, Zürich, Switzerland (
  • 2School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
  • 3School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, China

Atmospheric water vapor is an important greenhouse gas in the Earth’s atmosphere and significantly impact the thermodynamics of the atmosphere. Due to its dramatic spatio-temporal variability, knowing the three-dimensional (3D) distribution of it is a key goal of atmospheric observation that has been very difficult to attain. However, Global Navigation Satellite System (GNSS) tomography is a promising technique that retrieves the 3D observation of atmospheric water vapor using data from all satellite constellations with a dense station network. In the last decades, various tomography algorithms were developed based on the fixed-scale tomography (FST) system with an unchangeable tomographic domain and voxel. Here we demonstrate the development of a new adaptive variable-scale tomography (AVST) system to determine the optimal dynamic boundary of tomography area and the adaptive resolution of tomography voxel in different atmospheric layers. First, the optimal regular tomography region of each layer is constructed by the boundary optimal approach based on the convex hull algorithm. Subsequently, we define a water vapor index (WVI) and introduce a WVI invariance discretized principle to obtain the variable-scale voxels in different layers.

The proposed method is applied to reconstruct the 3D adaptive water vapor fields over Hong Kong region using the GNSS data in August 2017. For validations, we compared the tomographic water vapor profiles with the reference profiles from radiosonde, and assessed the tomographic overall distributions using independent ERA5 data. The results show that AVST approach is superior to the FST method in both water vapor profiles and 3D distributions, with the mean root-mean-square-error (RMSE) improved by 30% and 23%, respectively. Such improvements highlight the significant potential of the proposed principle for reconstructing the 3D adaptive atmospheric water vapor fields to advance rainfall forecast and meteorological research.

How to cite: Zhang, W., Moeller, G., Zhang, S., Zheng, N., and Ding, N.: Dynamic Tomography Principle: An Adaptive Variable-Scale Approach to GNSS Atmospheric Water Vapor Tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2067,, 2022.