Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm

Suqin Wu,Lucas Holden,Xiangrong Yan,Wenyuan Zhang,Yuchen Wang,Xin Liu,Shubi Zhang,Xiaoming Wang,Yu Zhang,Kefei Zhang,Nan Ding

doi:10.3390/rs12172744

Abstract

Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography.

Highlights

Water vapor plays an important role in the Earth’s weather and climate systems, the water cycle and the regulation of air temperature
Chen and Liu [3] compress the height of the tomographic region from 15 km to 8.5 km to introduce more Global Navigation Satellite Systems (GNSS) signals and move the tomographic region to the optimal location where the number of the voxels crossed by signals reaches its maximum
We focus on the improvement of the common node-based GNSS tomographic approach

Summary

Introduction

Water vapor plays an important role in the Earth’s weather and climate systems, the water cycle and the regulation of air temperature. Global Navigation Satellite Systems (GNSS) tomography is one of the primary techniques for determining the three-dimensional distribution of water vapor in the tomographic region. The most common methods used to determine the dimensions of the tomographic model only consider the GNSS signals’ distribution through the top boundary of the model This is based on the assumption that, for the selected model, any GNSS signals that pierce the top of the model must be distributed through the lower levels of the model. To address the above problems, a node-based innovative approach to dynamically determine the dimensions of the tomographic region at each epoch by the minimum bounding box algorithm is Remote Sens. This paper mainly discusses the new approach that improves node-based GNSS tomography with a minimum bounding box algorithm.

Observations Used in the Tomographic Model

Classical Tomographic Model

New Node-Based Tomographic Approach

Determination of a Tomographic Region

Convex Hull of the Pierce Points

Minimum Bounding Box Algorithm

Determination of the Position and Density of Nodes

Construction and Solution of Tomographic Equations

Test Results and Analysis