An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area

Zhao, Qingzhi; Yao, Yibin

doi:10.5194/angeo-35-87-2017

Cited by 23 publications

(15 citation statements)

References 31 publications

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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 follows:…”

Section: Gnss Tropospheric Tomographymentioning

confidence: 99%

“…Therefore, the preliminary result concluded from those studies needs further verification based on the observed multi-constellation GNSS data. Although some tomographic experiments have been performed using the observed multi-GNSS observations (Benevides et al, 2017;Dong and Shuanggen, 2018;Zhao et al, 2018), the influence of station density and different combinations of multi-GNSS observations on troposphere tomography, which is the focus of this study, have never been well investigated. In this paper, a method is proposed to determine the optimal division of voxels in the horizontal direction automatically according to the range of the tomography area as well as the number and distribution of GNSS stations.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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Abstract. Troposphere tomography, using multi-constellation observations from global navigation satellite systems (GNSSs), has become a novel approach for the three-dimensional (3-D) reconstruction of water vapour fields. An analysis of the integration of four GNSSs (BeiDou, GPS, GLONASS, and Galileo) observations is presented to investigate the impact of station density and single- and multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of the research area is determined in Hong Kong considering both the number of voxels divided, and the coverage rate of discretized voxels penetrated by satellite signals. The results show that densification of the GNSS network plays a more important role than using multi-constellation GNSS observations in improving the retrieval of 3-D atmospheric water vapour profiles. The root mean square of slant wet delay (SWD) residuals derived from the single-GNSS observations decreased by 16 % when the data from the other four stations are added. Furthermore, additional 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 and (2) the reconstructed quality of 3-D atmospheric water vapour using multi-constellation GNSS data can be improved by about 11 % when compared to the SWD estimated with precise point positioning, but this was not as high as expected.

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Section: Gnss Tropospheric Tomographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…To solve this problem, Rohm and Bosy estimated the outer part of the signal rays using the UNB3m model based on a ray-tracing method [17], Notarpietro et al used European center for medium-range weather forecasts (ECMWF) data to calculate the SWV outside the research area [19], and Chen and Liu applied the numerical weather prediction (NWP) profile data to estimate the slant wet delay (SWD) outside the modeling area [20]. Benevides et al proposed a geometric linear method using an empirically exponential negative function to increase the utilization of signal rays [21], whereas Yao et al introduced a unit scale factor model and reconstructed a sophisticated model for considering the signal rays penetrating from the side face of the study area [22,23].…”

Section: Introductionmentioning

confidence: 99%

A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

et al. 2019

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The spatio-temporal distribution of atmospheric water vapor information can be obtained by global positioning system (GPS) water vapor tomography. GPS signal rays pass through the tomographic area from different boundaries because the scope of the research region (latitude, longitude, and altitude) is designated in the process of tomographic modeling, the influence of the geographic distribution of receivers, and the geometric location of satellite constellations. Traditionally, only signal rays penetrating the entire tomographic area are considered in the computation of water vapor information, whereas those passing through the sides are neglected. Therefore, the accuracy of the tomographic result, especially at the bottom of the area, does not reach its full potential. To solve this problem, this paper proposes a new method that simultaneously considers the discretized tomographic voxels and the troposphere outside the research area as unknown parameters. This method can effectively improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals, especially by increasing the proportion of voxels penetrated. A tomographic experiment is implemented using GPS data from the Hong Kong Satellite Positioning Reference Station Network. Compared to the traditional method, the proposed method increases the number of voxels crossed by signal rays and the utilization of the observed data by 15.14% and 19.68% on average, respectively. Numerical results, including comparisons of slant water vapor (SWV), precipitable water vapor (PWV), and water vapor density profile, show that the proposed method is better than traditional methods. In comparison to the water vapor density profile, the root-mean-square error (RMS), mean absolute error (MAE), standard deviation (SD), and bias of the proposed method are 1.39, 1.07, 1.30, and −0.21 gm−3, respectively. For the SWV and PWV comparison, the RMS/MAE of the proposed method are 10.46/8.17 mm and 4.00/3.39 mm, respectively.

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“…Besides the GNSS tomography technique, the WR can also be retrieved by data assimilation which is based on numerical weather prediction (NWP) models (Perler et al, 2011b). The Weather Research and Forecasting (WRF) model is a state-of-the-art atmospheric modeling system that is used to simulate the dynamic processes of the atmosphere (Jankov et al, 2005;Carvalhoaabc, 2012). It is mainly developed and supported by the Mesoscale and Microscale Meteorology (MMM) Laboratory of the National Center for Atmospheric Research (NCAR).…”

Section: Introductionmentioning

confidence: 99%

Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

2019

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Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to accurately describe its three-dimensional (3-D) dynamical changes. The data assimilation technique and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting Data Assimilation (WRFDA) model (a representative of the data assimilation models) in retrieving wet refractivity (WR) in the Hong Kong area during a wet period and a dry period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delays. The WRFDA is used to assimilate the zenith tropospheric delay to improve the background data. The radiosonde data are used to validate the WR derived from the GNSS tomography, the WRFDA output, and the background data. The root mean square (rms) of the WR derived from the tomography results, the WRFDA output, and the background data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower accuracy in the dry period is mainly due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA only slightly improves the accuracy of the WR and that the WRFDA WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the WRFDA WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA, the retrieved WR is improved.

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An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area

Cited by 23 publications

References 31 publications

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

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

A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

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