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

Abstract

<p>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.</p><p>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.</p>