Water vapor is one of the primary greenhouse gases and significantly impacts the atmosphere. Water vapor is the most active meteorological element and varies rapidly in both the spatial and temporal domains. As a promising means, Global Navigation Satellite Systems (GNSS) tomography has been used to construct the 3D distribution of water vapor in high resolutions. Currently, in the most commonly used node parameterization approaches, the region for the 3D modeling has a preset fixed regular shape for all tomographic epochs. As a result, too many unknown parameters need to be estimated and thus to degrade the performance of the tomographic solution. In this study, an innovative node parameterization approach using a combination of three meshing techniques to dynamically adjust both the boundary of the tomographic region and the position of nodes at each tomographic epoch is proposed. The three meshing techniques were boundary extraction, Delaunay triangulation, and force‐displacement algorithm. The performance of the tomographic model resulting from the new approach was tested using one month GNSS data in May 2015 from the Hong Kong GNSS network and was compared against that of the conventional node parameterization approach. The reference for the validation of the accuracy of the test results were the radiosonde measurements from King's Park Meteorological Station (HKKP) in Hong Kong. Results showed that in terms of root‐mean‐square error the accuracy of the new approach significantly improved in comparison to the traditional approach.

Water vapor is the basic parameter used to describe atmospheric conditions. It is rarely contained in the atmosphere during the water cycle, but it is the most active element in rapid space–time changes. Measuring and monitoring the distribution and quantity of water vapor is a necessary task. GPS tomography is a powerful means of providing high spatiotemporal resolution of water vapor density. In this paper, a spatial structure model of a humidity field is constructed using voxel nodes, and new parameterizations for acquiring data about water vapor in the troposphere via GPS are proposed based on inverse distance weighted (IDW) interpolation. Unlike the density of water vapor that is constant within a voxel, the density at a certain point is determined by IDW interpolation. This algorithm avoids the use of horizontal constraints to smooth voxels that are not crossed by satellite rays. A prime number decomposition (PND) access order scheme is introduced to minimize correlation between slant wet delay (SWD) observations. Four experimental schemes for GPS tomography are carried out in dry weather from 2 to 8 August 2015 and rainy days from 9 to 15 August 2015. Using 14 days of data from the Hong Kong Satellite Positioning Reference Station Network (SatRef), the results indicate that water vapor density derived from 4-node methods is more robust than that derived from that of 8 nodes or 12 nodes, or that derived from constant refractivity schemes and the new method has better performance under stable weather conditions than unstable weather (e.g., rainy days). The results also indicate that an excessive number of interpolations in each layer reduce accuracy. However, the accuracy of the tomography results is gradually reduced with increases in altitude below 7000 m. Moreover, in the case of altitudes between 7000 m and the upper boundary layer, the accuracy can be improved by a boundary constraint.