An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area

Abstract

The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.

A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

“…A similar idea of decreasing the number of empty voxels is proposed by Yao and Zhao (2016), who identify the optimal top boundary according to the radiosonde data and introduce the nonuniform symmetrical division of horizontal voxels. Haji-Aghajany et al (2020b) reduce the number of unknown parameters and empty voxels by merging voxels based on the WRF model, which improves the ill-conditioned tomographic equations. In addition, the studies in Yang et al (2019), , Zhao et al (2018), Zhao et al (2020), and Zhang et al (2020b) propose approaches to incorporate the GNSS signals passing through the side face of the tomography area.…”

The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy

“…The first tropospheric tomography studies to obtain water vapor field were performed by Flores and Hirahara (Flores et al 2000;Hirahara 2000). In the following years, many researchers have tried to optimize this technique (Bender et al 2011;Rohm et al 2014;Yao and Zhao 2016;Haji-Aghajany and Amerian 2017; Haji-Aghajany and Amerian 2018; Heublein et al 2019;Haji-Aghajany et al 2020a). A comprehensive overview of the advantages and deficiencies of current tomography models could be found in Brenot et al (2020).…”

LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography

“…Generally, it can be solved by adding constraint equations [3][4][5]. In addition, optimizing the voxel distribution [6], using rays passing from the side [7], and fusing GNSS station observations outside the tomographic region [8], were all proven to be effective in reducing the proportion of empty voxels in a tomographic region. A new algorithm for discrete tomography regions into inverted cones also proved to be very effective [9].…”