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.

This paper proposes an optimal tropospheric tomography approach with the support of an auxiliary area, which has the ability to use the signals crossing out from the top boundary of the tomographic area. Additionally, the top height of the tomography body is determined based on the average water vapour distribution derived from the COSMIC data. The compared result reveals the superiority of the proposed method when compared to the conventional method.

This paper captures the signature of heavy rainfall events using the 2-d-/4-d water vapour information derived from GNSS measurement in Hong Kong. The paper first analyzed the relationship between the two-dimensional (2-d) precipitable water vapour (PWV) and rainfall. And then, the four-dimensional (4-d) variations of atmospheric water vapour derived from the GNSS tomographic technique are discussed, especially in the vertical irection. Finally, some interesting results are found and presented.

A troposphere tomographic method has been proposed considering the signal rays penetrating from the side of the area of interest. Given the method above needs the establishment of a unit scale factor model using the radiosonde data at only one location in the research area, an improved approach is proposed by considering the reasonability of modelling data and the diversity of the modelling parameters for building a more accurate unit scale factor model.