A new approach for GNSS tomography from a few GNSS stations

Ding, Nan; Zhang, Shubi; Wu, Suqin; Wang, Xiaoming; Kealy, Allison; Zhang, Kefei

doi:10.5194/amt-11-3511-2018

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…Following the above steps, an integro-interpolation algorithm [10] is used to build observation equations and to estimate water vapor density (or SWV) at each tomographic node. The value of SWVs can be expressed as the water vapor densities at the corresponding tomographic nodes.…”

Section: Estimation Of Gnss Tomographic Parametersmentioning

confidence: 99%

“…Global Navigation Satellite System (GNSS) tomography [1] helps achieve this, due to its ability to provide high resolution vertical and horizontal measurements of water vapor. Numerous studies have utilized this approach and have taken advantage of the ongoing improvements in Continuously Operating Reference Stations network (CORS)density [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. GNSS tomographic techniques were tested as early as 2000 through GNSS networks with 16 and 24 stations [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…GNSS tomographic techniques were tested as early as 2000 through GNSS networks with 16 and 24 stations [2,3]. Currently, studies on GNSS tomography mainly focus on the use of data from a GNSS network consisting of several stations (most more than 8 GNSS stations) [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] with dense and uniform distribution in a region. However, all these studies have not considered the variable number of GNSS stations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network

Wang

Ding

et al. 2020

Remote Sensing

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Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined by the distribution of the GNSS signals skywards from a network of ground based GNSS stations for all epochs of measurements. However, in reality at each epoch the shape of the GNSS tomographic region is more likely to be an inverted cone. Unfortunately, this fixed conic tomographic region does not properly reflect the fact that the GNSS signal changes quickly over time. Therefore a dynamic or adaptive tomographic region is better suited. In this study, a new approach that adjusts the GNSS tomographic model to adapt the size of the GNSS network is proposed, which referred to as The High Flexibility GNSS Tomography (HFGT). Test data from different numbers of the GNSS stations are used and the results from HFGT are compared against that of radiosonde data (RS) to assess the accuracy of the HFGT approach. The results showed that the new approach is feasible for different numbers of the GNSS stations when a sufficient and uniformed distribution of GNSS signals is used. This is a novel approach for GNSS tomography.

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Section: Estimation Of Gnss Tomographic Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network

Wang

Ding

et al. 2020

Remote Sensing

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“…Xia et al (2013) combine iterative and non-iterative techniques. Instead of using voxels in water vapor tomographies with a small number of GNSS sites, Ding et al (2018) discretize the tomographic field based on the perimeter of the tomographic boundary on the plane and based on meshing techniques. They then determine tomographic fields by means of fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs.…”

Section: Related Workmentioning

confidence: 99%

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

2020

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Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry's effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions' variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

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“…-P2L22: i.e. Rohm et al (2014) also used a Kalman filter for GNSS tomography -a novel and interesting approach for a geometric discretization of the tomographic network can be found in Ding et al (2018) Section 3.3:…”

Section: Interactive Commentmentioning

confidence: 99%

Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing - review

Anonymous

2019

Preprint

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Although the manuscript is overally well written and generally contains work which is worthy to be published, I have some comments and questions including three major ones. I would like to ask the authors to adress all of them. Major comments: Section 2: Related work I miss references to some of the important works related to GNSS tomography. To be more specific:-I mainly miss the work of Bender and Raabe (2007) dealing with preconditions to GNSS tomography and Bender et al. (2011) presenting evaluation of benefit using multi-GNSS tomography instead of GPS-only tomography. Paper by C1

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A new approach for GNSS tomography from a few GNSS stations

Cited by 10 publications

References 26 publications

A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network

A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing - review

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