An improved GNSS tropospheric tomography method with the GPT2w model

Zhao, Qingzhi; Yao, Wanqiang; Yao, Yibin; Li, Xin

doi:10.1007/s10291-020-0974-4

Cited by 32 publications

(23 citation statements)

References 33 publications

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“…Based on the existing research [15,24], we determined the horizontal grid scheme shown in Figure 6. The figure shows that the selected horizontal domain with a scope of 22.18 • -22.60 • N, 113.88 • -114.36 • E, was discretized into 7 × 8 grids, 7 in the latitudinal and 8 in the longitudinal direction.…”

Section: Grid Schemementioning

confidence: 99%

“…Because of the geometric distribution of GNSS constellations and ground receivers, insufficient rays cross each voxel of interest [14,15]. This leads to ill-posed problems, which arise in tomographic equations.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Zhao et al [14] utilized signals from receivers located outside of the tomographic region with the truncation factor model (TFM) for the maximum number of valid rays. Furthermore, signals leaving the side face of the tomographic zone can participate to establish the tomography equation with the help of empirical Global Pressure and the Temperature 2 wet (GPT2w) model [15]. Given the effort of many researchers, GNSS-WV tomography has experienced significant development.…”

Section: Introductionmentioning

confidence: 99%

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A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

Yao

Liu

et al. 2020

Remote Sensing

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Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method.

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Section: Grid Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

Yao

Liu

et al. 2020

Remote Sensing

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“…This problem causes a rank deficiency in the coefficient matrix. Some previous researchers proposed the methods of using the signals penetrating from the side face of tomography area (Yao and Zhao 2016;Zhao and Yao 2017;Zhao et al, 2020) and using the data of GNSS observations outside the study area to solve this problem (Zhao et al, 2019). However, in general, the use of constraints to solve the problem is inevitable.…”

Section: Introductionmentioning

confidence: 99%

B-spline function-based approach for GPS tropospheric tomography

2020

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“…Tropospheric tomography is generally performed using the voxel-based method. Many researchers have tried to improve the accuracy of the results by optimizing different aspects of the voxel-based troposphere tomography: (1) improving the inversion process [6,7], (2) reformatting the model geometry [8][9][10][11][12], (3) applying advance optimization techniques [13], (4) using the signals penetrating into the side face of the tomography model and using the data from Global Navigation Satellites System (GNSS) observations outside the study region [8,10,[14][15][16]. An important overview of the benefits and deficiencies of current tomography models could be found in [17].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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An improved GNSS tropospheric tomography method with the GPT2w model

Cited by 32 publications

References 33 publications

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

B-spline function-based approach for GPS tropospheric tomography

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

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