An Optimal Tropospheric Tomography Method Based on the Multi-GNSS Observations

Zhao, Qingzhi; Yao, Yibin; Cao, Xinyun; Zhou, Feng; Xia, Pengfei

doi:10.3390/rs10020234

Cited by 27 publications

(15 citation statements)

References 45 publications

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“…The RS-WV shown in Figure 10a is more even than that in Figure 10b, which is near the ideal WV distribution in the vertical direction. Zhang et al [24] stated that the WV with an even distribution is beneficial for good tomography results obtained by the GNSS-WV tomography. Hence, at the epoch in Figure 10a, the WV profiles obtained by the two tomography methods were in good agreement with that obtained by the RS-Method.…”

Section: Tomography Results For the Comparison Between Methods 1 Andmentioning

confidence: 99%

“…The tomographic window has reached widespread applications in previous studies [24][25][26]. This "window" is based on the assumption that the WV of interest remains stationary across a period, and the length of the period determines the size of the window.…”

Section: General Tomographic Window and Existing Problemsmentioning

confidence: 99%

“…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%

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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: Tomography Results For the Comparison Between Methods 1 Andmentioning

confidence: 99%

Section: General Tomographic Window and Existing Problemsmentioning

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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“…Therefore, the latter is obtained by subtracting the ZHD from ZTD. In our study, the observed multi-constellation GNSS data are processed using the multi-constellation GNSS Precise Point Positioning (PPP) software with precise orbit and clock error products (Zhao et al, 2018). Consequently, the SWD can be expressed as:…”

Section: Gnss Tropospheric Tomographymentioning

confidence: 99%

Influence of station density and multi-constellation GNSS observations on troposphere tomography

Zhao¹,

Zhang²,

Yao³

2018

Preprint

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Abstract. Troposphere tomography, using multi-constellation GNSS observations, has become a novel approach for the three-dimensional (3-d) reconstruction of water vapour fields. An analysis of the integration of four Global Navigation Satellite Systems (BeiDou, GPS, GLONASS and Galileo) observations is presented to investigate the impact of station density and single/multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of research area is determined in Hong Kong, which considers both the number of voxels divided, and the coverage rate of discretized voxels penetrated by satellite signals. Tomography experiment reveals that the influence of station density in a GNSS network is more significant than the multi-constellation GNSS observations on the reconstruction of 3-d atmospheric water vapour profiles. Compared to the tomographic result from the multi-constellation GNSS (BeiDou, GPS, GLONASS and Galileo) observations, the RMS of SWD residuals derived from the single-GNSS observations has been decreased by 16 % when the data from the other four stations are added. Furthermore, more experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations; (2) the tomographic result with multi-constellation GNSS data can improve the reconstructed quality of 3-d atmospheric water vapour by the largest RMS value of about 11 % when compared to the PPP-estimated SWD, but this was not as high as was expected.

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“…At this resolution, the inversion problem is ill-posed with several possible solutions. In the last decade, much effort has been applied to obtain a unique solution by adding additional constraints Mashhadi-Hossainali, 2014, 2015;Bender et al, 2011;Benevides et al, 2016;Flores et al, 2000;Gradinarsky and Jarlemark, 2004;Guo et al, 2016;Manning et al, 2012;Möller, 2017;Rohm, 2012b;Rohm and Bosy, 2009;Rohm et al, 2013;Troller, 2004a;Trzcina and Rohm, 2019;Xia et al, 2013;Zhao et al, 2018Zhao et al, , 2019. Flores et al (2000) applied horizontal constraints, vertical constraints and boundary conditions using the Singular Value Decomposition (SVD) to solve the tomography problem.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Virtual Reference Station Constraints for GNSS Tropospheric Tomography in Austria Region

Adavi

Weber

2019

Adv. Geosci.

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Abstract. One of the most promising methods of GNSS meteorology is GNSS Tomography. This method can be used for the determination of water vapor distribution, which contributes to the reliability of weather forecasting and early warning of severe weather. Therefore, GNSS Tomography is a valuable source of information for meteorological and weather forecast. The system of equations of this problem is mixed-determined because propagated signals do not pass through some of the model elements within the area of interest. Consequently, the normal matrix is close to singular without any unique solution. To avoid singularity and achieve a unique solution, additional sources or horizontal and/or vertical constraints are usually applied. Here, three schemes have been considered for remedying the rank deficiency of the problem. In the first scheme, minimum horizontal and vertical constraints were imposed on the system of observation equations. Then, we have defined three schemes to evaluate the impact of Virtual Reference Stations (VRS) in comparison to horizontal and vertical constraints in the sparse GNSS network. Within a network of Austrian GNSS reference stations these schemes have been analyzed and validated with available radiosonde profiles for the period DoY 245–256 in 2017. According to our results, the consistency of the estimated refractivity field with radiosonde profiles in the dense GNSS network was generally better (RMSE 2.80 ppm) than for the two other schemes in the period of interest. Moreover, in the sparse GNSS network, the average of RMSE for schemes with VRS stations and constraints equation was about 3.02 and 3.27 ppm, respectively. Hence, the obtained results illustrate that applying VRS stations in the sparse GNSS network can lead to a better solution in comparison to applying horizontal and vertical constraints.

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An Optimal Tropospheric Tomography Method Based on the Multi-GNSS Observations

Cited by 27 publications

References 45 publications

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

Influence of station density and multi-constellation GNSS observations on troposphere tomography

Evaluation of Virtual Reference Station Constraints for GNSS Tropospheric Tomography in Austria Region

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