Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region

Abstract

Water vapor is a dominant greenhouse gas. It significantly impacts the atmosphere by trapping heat and infrared radiation. The greenhouse effect is essential for life on Earth but can also be harmful. Although the amount of water vapor in the atmosphere is not much during the water cycle, it is the most active element in rapid changes in both spatial and temporal domains. GNSS tomography’s ability to model the high-resolution 3D distribution of water vapor is a promising means of measuring and monitoring the spatial-temporal variation of water vapor. This study developed and tested a new GNSS tomographic model using adaptive voxel parameterization. It uses a 3D traversal algorithm to dynamically determine the position of voxels at each tomographic sampling epoch. It means that the new algorithm can exclude the voxels that no GNSS signals pass through, reducing the influence of such voxels in the construction of the tomographic model. This study provides a new approach to investigating the inversion of atmospheric water vapor. The experiment used one-month data from the Hong Kong network in September 2020, and the results were compared with the general system. The local radiosonde data is a reference for verification of the two approaches. The mean root-mean-square error (RMSE) and IQR of the water vapor profiles derived from AAR are decreased by 55% and 48% with respect to the GFR results, respectively. The results show that the accuracy of the new method outperforms the general approach in the result statistics. The successful implementation of the research has significant potential to drive the development of GNSS tomography in the study of weather and climate change.