Accuracy and reliability of tropospheric wet refractivity tomography with GPS, BDS, and GLONASS observations

Zhao, Qingzhi; Yao, Yibin; Cao, Xinyun; Yao, Wanqiang

doi:10.1016/j.asr.2018.01.021

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…In terms of tomography accuracy, the scheme with LEO has improved the tomography accuracy, but the improvement of water vapor tomography accuracy is not as large as expected. This conclusion also echoes some previous studies [18,19]. The reason for this is because of the 30-min observation, so there are already many observation rays in the tomographic area.…”

Section: Discussionsupporting

confidence: 89%

“…The potential of multi-GNSS tomography was verified by the triple system tomography experiments of GPS, BDS, and GLONASS [18]. Zhao et al [19] studied the accuracy and stability of GNSS tomography, and noted that the accuracy improvement did not meet the expectations, in addition to the fact that 50% of the voxels were still unobserved under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Xiong

Ren

et al. 2021

Remote Sensing

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Global navigation satellite systems (GNSS) water vapor tomography is an important technique to obtain the three-dimensional distribution of atmospheric water vapor. The rapid development of low Earth orbit (LEO) constellations has led to a richer set of observations, which brings new expectations for water vapor tomography. This paper analyzes the influence of LEO constellation-augmented multi-GNSS(LCAMG)on the tomography, in terms of ray distribution, tomography accuracy, and horizontal resolution, by simulating LEO constellation data. The results show that after adding 288 LEO satellites to GNSS, the 30-min ray distribution effect of GNSS can be achieved in 10 min, which can effectively shorten the observation time by 66.7%. In the 10-min observation time, the non-repetitive effective observation value of LCAMG is 2.38 times that of GNSS, and the accuracy is 1.27% higher than that of GNSS. Compared with GNSS and the global positioning system (GPS), at a horizontal resolution of 13 × 14, the proportion of empty voxels in LCAMG reduces by 5.22% and 22.53%, respectively.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

Xiong

Ren

et al. 2021

Remote Sensing

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“…Initially presented by Candès (2006), Donoho (2006), Baraniuk (2007), and Candès and Wakin (2008) for the image or signal recovery from a number of samples below the desired resolution or the Nyquist rate, CS has been, since then, applied to many remote sensing problems in which sparse signals occur. For example, Potter et al (2010) and Alonso et al (2010) describe the use of CS for synthetic aperture radar (SAR) imaging, Pruente (2010) applies CS for ground-moving target identification, Zhu and Bamler (2010), Budillon et al (2011), Aguilera et al (2013), and Zhu and Bamler (2014) apply CS to SAR tomography, and Li and Yang (2011), Zhu and Bamler (2013), Grohnfeldt et al (2013), Jiang et al (2014), and Zhu et al (2016) use CS for pan-sharpening and hyperspectral image enhancement. When compared to classical LSQ adjustments usually applying L 2 -norm regularizations, compressive sensing and sparse reconstruction based on a small number of measurements led to promising results.…”

Section: Related Workmentioning

confidence: 99%

“…They then use this information as a precondition for an optimal tomographic reconstruction and in order to identify regions that are well covered by GPS slant paths. Although Bender et al (2011b) and Zhao et al (2019) state that changing the observing geometry by combining multi-GNSS observations instead of GPS-only observations does not substantially improve the reconstruction quality, Rohm (2012) realizes that the uncertainty of the tomographic solution is largely influenced by the mathematical properties of the design matrix, depending itself on the observing geometry. With the aim of giving advice for the installation of new permanent sites and for the solution of future water vapor tomographies, this work therefore investigates the observing geometry's effect on the quality of both an LSQ solution and a CS solution to the tomographic system.…”

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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“…Modeling the ZTD using a single GNSS system has been proposed by a number of researchers (e.g., Dousa and Bennitt, 2013;Dousa and Vaclavovic, 2014;Ahmed et al, 2016;Mendez Astudillo et al, 2018;Oikonomou et al, 2018;Ssenyunzi et al, 2019). Moreover, recently, the ZTD has been modeled using the multi-constellation GNSS (e.g., Lu et al, 2015;Lu et al, 2017;Ding et al, 2017;Ding et al, 2018;Hu et al, 2018;Li et al, 2018;Pan and Guo, 2018;Zheng et al, 2018;Zhao et al, 2019). Lu et al (2015) developed a real-time ZTD estimation model using GPS and the BeiDou observations.…”

Section: Introductionmentioning

confidence: 99%

Assessment of GNSS PPP-Based Zenith Tropospheric Delay

Abdelazeem

El-Rabbany

2020

Artificial Satellites

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This study assesses the precision of zenith tropospheric delay (ZTD) obtained through triple-constellation global navigation satellite system (GNSS) precise point positioning (PPP). Various ZTD estimates are obtained as by-products from GPS-only, GPS/Galileo, GPS/BeiDou, and triple-constellation GPS/Galileo/BeiDou PPP solutions. Triple-constellation GNSS observations from a number of globally distributed reference stations are processed over a period of seven days in order to investigate the daily performance of the ZTD estimates. The estimated ZTDs are then validated by comparing them with the International GNSS Service (IGS) tropospheric products and the University of New Brunswick (UNB3m) model counterparts. It is shown that the ZTD estimates agree with the IGS counterparts with a maximum standard deviation (STD) of 2.4 cm. It is also shown that the precision of estimated ZTD from the GPS/Galileo and GPS/Galileo/BeiDou PPP solutions is improved by about 4.5 and 14%, respectively, with respect to the GPS-only PPP solution. Moreover, it is found that the estimated ZTD agrees with the UNB3m model with a maximum STD of 3.1 cm. Furthermore, the GPS/Galileo and GPS/Galileo/BeiDou PPP enhance the precision of the ZTD estimates by about 6.5 and 10%, respectively, in comparison with the GPS-only PPP solution.

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Accuracy and reliability of tropospheric wet refractivity tomography with GPS, BDS, and GLONASS observations

Cited by 16 publications

References 32 publications

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

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

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

Assessment of GNSS PPP-Based Zenith Tropospheric Delay

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