Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique

Abstract

In most previous studies of tropospheric tomography, water vapor is assumed to have a homogeneous distribution within each voxel. The parameterization of voxels can mitigate the negative effects of the improper assumption to the tomographic solution. An improved parameterized algorithm is proposed for determining the water vapor distribution by Global Navigation Satellite System (GNSS) tomography. Within a voxel, a generic point is determined via horizontal inverse distance weighted (IDW) interpolation and vertical exponential interpolation from the wet refractivities at the eight surrounding voxel nodes. The parameters involved in exponential and IDW interpolation are dynamically estimated for each tomography by using the refractivity field of the last process. By considering the quasi-exponential behavior of the wet refractivity profile, an optimal algorithm is proposed to discretize the vertical layers of the tomographic model. The improved parameterization algorithm is validated with the observational data collected over a 1-month period from 124 Global Positioning System (GPS) stations of Hunan Province, China. Assessments by GPS, radiosonde, and European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) data, demonstrate that the improved model outperforms the traditional nonparametric model and the parameterized model using trilinear interpolation. In the assessment by GPS data, the improved model performs better than the traditional model and the trilinear parameterized model by 54% and 10%, respectively. Such improvements are 31% and 10% in the validation by radiosonde profiles. In comparison with the ERA5 reanalysis, the improved model yields a minimum overall root mean square (RMS) error of 8.94 mm/km, while those of the traditional and trilinear parametrized models are 10.79 and 9.73 mm/km, respectively. The RMS errors vertically decrease from ~20 mm/km at the bottom to ~5 mm/km at the top layer.

Monitoring urban heat island intensity based on GNSS tomography technique

“…The wet refractivity of a generic point is expressed by a weighted mean of the wet refractivity values at the eight voxel nodes based on Newton-Cotes quadrature, where the point is located (Perler et al 2011;Chen et al 2020). In this study, we used the fourth-order Newton-Cotes quadrature to obtain the wet refractivity of each voxel from the wet refractivity values at the eight voxel nodes.…”

“…In terms of the grid division, Chen and Liu (2014) proposed a novel method to establish the optimal horizontal distribution of voxels. Chen et al (2020) developed an improved parameterized algorithm to refine the tropospheric tomographic model to enhance the performance of the wet refractivity reconstruction. Zhang et al (2022a, b) developed a GNSS combining remote sensing (RS) tomography model to exploit the adding value of RS measurements to GNSS tomography.…”

A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

“…By combining the above four matrix equations, the unknown quantities of water vapor density can be calculated in each grid using the singular value decomposition solving method of MATLAB [56], [U, S, V] = svd (C), where C represents the coefficient matrix on the left side of the matrix Equation (33), and the V matrix is the result of the water vapor density in the model grid.…”

“…Benevides et al [30] used multi-GNSS observations to tackle this problem, while Yao and Zhao [31] added the rays passing through the side of the tomographic model into the observation equation matrix to increase the stability of the calculation. In recent years, many researches have focused on the model building technology itself, some new methods and techniques such as the least-squares and compressive sensing [32], improved parameterized tropospheric tomographic technique [33], and adaptive simultaneous iterative reconstruction technique [34] have been applied to the model. In addition, many studies resolved the problems of solving observing equations [35,36] while some other studies took the observations of multi-satellite navigation systems as input [37,38] to promote the efficiency of the model.…”

Recent Crustal Deformation Based on Interpolation of GNSS Velocity in Continental China

“…With the rapid progress of Global Navigation Satellite System (GNSS) technology, as well as the continuous improvement of GNSS accuracy and space-time resolution, GNSS has gradually become an essential observation means in geoscience research, especially in crustal deformation research. Deformation research based on GNSS is ongoing, which provides a data reference basis for the different scales of subsequent deformation research [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Although many scholars have studied the velocity field data of GNSS stations, with the increasing time span of the data, previous studies have a certain timeliness.…”