Approach to leveraging real-time GNSS tomography usage

Sá, André; Rohm, Witold; Fernandes, Rui; Trzcina, Estera; Bos, M. S.

doi:10.1007/s00190-020-01464-7

Cited by 14 publications

(5 citation statements)

References 49 publications

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“…In general, the main objective of developing GNSS tomography is to retrieve 3D water vapor distribution and then explore its applications to extreme weather research and tropospheric delay correction using tomographic products (Zhang et al 2020a, b,c). This is achievable through developing the (near) real-time GNSS tomography technique (Sá et al 2021), which requires a fast tomography equation solving and the (near) real-time tropospheric parameters including ZTD and horizontal gradients (Zhang et al 2020a, b,c).…”

Section: Performances Of Hogt Under Different Time Intervalsmentioning

confidence: 99%

“…Furthermore, due to the small tomography area in Hong Kong and Xuzhou, there are only 450 voxels for each tomography interval in both regions. As a consequence, although the time interval is set to 30 min, the mean processing time of solving equations is less than 2 s. Furthermore, on a wet day, atmospheric water vapor may show rapid and strong variations within a few minutes, which can be captured if tomographic model can retrieve accurate water vapor distribution in short time interval (Sá et al 2021). This is of great relevance for the accurate weather monitoring and forecasting.…”

Section: Performances Of Hogt Under Different Time Intervalsmentioning

confidence: 99%

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A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

Zhang

Zhang²,

Chang³

et al. 2021

J Geod

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Accurate three-dimensional (3D) atmospheric water vapor distribution, which plays a crucial role in understanding the meteorological phenomena and hazards, has been successfully retrieved using Global Navigation Satellite System (GNSS) tomography technique. Presently, the problem of the ill-posed tomography system, that results from poor GNSS acquisition geometry, remains a vital issue to be solved. In this paper, we develop a new hybrid observation GNSS tomography (HOGT) method, which constructs and introduces the virtual signals, inverted to the real rays, to address the acquisition geometry defect. Within the HOGT, the slant wet delay (SWD) of the virtual inverted signal (VIS) is estimated by means of the tropospheric parameters derived from the hourly ERA5 data using the ray-tracing algorithm. Two designed experiments, based on the dense and sparse GNSS networks (corresponding to Hong Kong and Xuzhou), respectively, are implemented to assess the performance of HOGT for the different networks. The results reveal that HOGT provides a more robust observation geometry and more accurate water vapor distribution than the traditional tomography model, with the mean number of the crossed voxels enhanced by 26.45% and 27.11% for the two networks, and the average root-mean-square error (RMSE) of the tomography solutions improved by 18.18% and 38.28% in the two areas, respectively. Furthermore, HOGT shows a significant improvement close to the Erath's surface from 0 to 2 km, implying its superior capability to optimize the accuracy of tomography results. An additional experiment to investigate the performance of the proposed method under different time resolutions demonstrates that HOGT can be promised to retrieve more accurate water vapor distribution over short time intervals, especially for rainy days with an interval of 10 min or even shorter, which highlights the interest in tomography solutions for improving the understanding of severe weather. KeywordsGlobal Navigation Satellite System (GNSS) • Hybrid observation GNSS tomography (HOGT) • Virtual inverted signals (VIS) • Dense and sparse networks • ERA5 • Radiosonde B Shubi Zhang

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Section: Performances Of Hogt Under Different Time Intervalsmentioning

confidence: 99%

Section: Performances Of Hogt Under Different Time Intervalsmentioning

confidence: 99%

A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

Zhang

Zhang²,

Chang³

et al. 2021

J Geod

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“…However, these developments could not be made without the inclusion of extraneous data from different sources, namely from NWP analysis or forecasts, from radiosonde observations or other remote sensing platforms, imposing direct constraints on the water vapor field. The feasibility of doing these inversions in real time was recently assessed (Sá et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A New Unconstrained Approach to GNSS Atmospheric Water Vapor Tomography

Miranda

Mateus

2021

Geophysical Research Letters

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A new atmospheric tomographic model totally based on Global Navigation Satellite System (GNSS) observations is proposed and tested against field observations. The method does not require a first guess, does not contain specific constraints on the variability of water vapor density inside the tomographic domain, and is able to produce reasonable results at 6 km horizontal and 500 m vertical resolutions, from short (30 min) GNSS data samples. The inversion method uses the Moore-Penrose pseudoinverse, which is made possible by increasing the rank of the design matrix through angular interpolation and extrapolation of the observations. Comparisons against 30 consecutive 4-h radiosonde observations and model simulations suggest the ability of the method to detect inversions and local maxima aloft, and behave sensibly in the far-field. Further improvements from this method may be expected from higher density and multi-constellation networks.Plain Language Summary Knowing the three-dimensional distribution of water vapor is a key goal of atmospheric observation that has been very difficult to attain, given its space and time variability. A new method of water vapor tomography is proposed, exclusively based on Global Navigation Satellite System observations, such as GPS, which is found to lead to sensible results. These results suggest a feasible tomographic system, using data from all satellite constellations (GPS, Glonass, Beidou, and Galileo) with a dense network of ground stations.

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“…In recent years, many researches have focused on the model building technology itself, some new methods and techniques such as the least-squares and compressive sensing [32], improved parameterized tropospheric tomographic technique [33], and adaptive simultaneous iterative reconstruction technique [34] have been applied to the model. In addition, many studies resolved the problems of solving observing equations [35,36] while some other studies took the observations of multi-satellite navigation systems as input [37,38] to promote the efficiency of the model. However, few studies have focused on the efficiency of signal line indexing in the tomography model which also needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

Liu

Zhong³

et al. 2021

Remote Sensing

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A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.

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Approach to leveraging real-time GNSS tomography usage

Cited by 14 publications

References 49 publications

A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

A New Unconstrained Approach to GNSS Atmospheric Water Vapor Tomography

A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

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