A method to improve the utilization of GNSS observation for  water vapor  tomography

Yao, Yibin; Zhao, Qingzhi; Zhang, Boen

doi:10.5194/angeo-34-143-2016

Cited by 34 publications

(17 citation statements)

References 29 publications

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“…Over the past decade, numerous studies demonstrated the potential of GNSS tomography to retrieve 4-D humidity fields. Unfortunately, because the quality of the reconstructed profiles is limited by the constellation of GNSS satellites, the geographic distribution of ground-based receivers, and observation errors (Chen and Liu, 2014;Rohm et al, 2014;Shangguan et al, 2013;Yao et al, 2016), some voxels may not be crossed by any signal during a tomographic process. Consequently, this will lead to an ill-posed inverse problem with incomplete input data.…”

Section: Introductionmentioning

confidence: 99%

“…The methods for solving the abovementioned problems can be broadly divided into four categories: (1) enhancement of the precision of SWD using the "zero differences" (ZDs) technique (Alber et al, 2000;Seko et al, 2004); (2) addition of constraint conditions to tomographic models, e.g., horizontal, vertical, and boundary constraint conditions (Flores et al, 2000;Hirahara, 2000;Perler, 2011;Rohm and Bosy, 2009;Seko et al, 2000;Song et al, 2006); (3) usage of additional extra observations through RINEX met files, zenith wet delay, WVR, RS, and voxel-optimized regional water vapor tomography (Bi et al, 2006;Chen and Liu, 2014;Jiang et al, 2014;Rocken et al, 1993;Rohm et al, 2014;Yao et al, 2016); and (4) new algorithms to improve inversion quality, such as singular value decomposition (SVD), the wet refractivity Kalman filter (KF), algebraic reconstruction techniques (ARTs), and the parameterization of voxels (volumetric pixels) based on trilinear and spline functions (Bender et al, 2011;Flores et al, 2001;Gradinarsky, 2002;Gradinarsky and Jarlemark, 2004;Nilsson and Gradinarsky, 2006;Rohm et al, 2013;Shangguan et al, 2013). At present, we are focused on replacing divided voxel-based traditional methods with new, parameterized approaches.…”

Section: Introductionmentioning

confidence: 99%

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New parameterized model for GPS water vapor tomography

Ding

Zhang

2017

Ann. Geophys.

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Abstract. Water vapor is the basic parameter used to describe atmospheric conditions. It is rarely contained in the atmosphere during the water cycle, but it is the most active element in rapid space-time changes. Measuring and monitoring the distribution and quantity of water vapor is a necessary task. GPS tomography is a powerful means of providing high spatiotemporal resolution of water vapor density. In this paper, a spatial structure model of a humidity field is constructed using voxel nodes, and new parameterizations for acquiring data about water vapor in the troposphere via GPS are proposed based on inverse distance weighted (IDW) interpolation. Unlike the density of water vapor that is constant within a voxel, the density at a certain point is determined by IDW interpolation. This algorithm avoids the use of horizontal constraints to smooth voxels that are not crossed by satellite rays. A prime number decomposition (PND) access order scheme is introduced to minimize correlation between slant wet delay (SWD) observations. Four experimental schemes for GPS tomography are carried out in dry weather from 2 to 8 August 2015 and rainy days from 9 to 15 August 2015. Using 14 days of data from the Hong Kong Satellite Positioning Reference Station Network (SatRef), the results indicate that water vapor density derived from 4-node methods is more robust than that derived from that of 8 nodes or 12 nodes, or that derived from constant refractivity schemes and the new method has better performance under stable weather conditions than unstable weather (e.g., rainy days). The results also indicate that an excessive number of interpolations in each layer reduce accuracy. However, the accuracy of the tomography results is gradually reduced with increases in altitude below 7000 m. Moreover, in the case of altitudes between 7000 m and the upper boundary layer, the accuracy can be improved by a boundary constraint.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New parameterized model for GPS water vapor tomography

Ding

Zhang

2017

Ann. Geophys.

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“…The first is SWD, which is the same input data used by [2] and [6] for obtaining the atmospheric wet refractivity fields. Another input parameter is SWV, which can be used to reconstruct the 3-d information of water vapour field [14,18,19]. [20] has proved that the two different classes of tomographic results can be interchangeably converted.…”

Section: Principle Of the Proposed Parameterised Approach For Tropospmentioning

confidence: 99%

Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach

Zhao

Yao

2018

Remote Sensing

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Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30 • is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m 3 and 0.92/−0.08 g/m 3 , respectively, while the values of the traditional method are 1.33/0.38 g/m 3 and 1.59/0.40 g/m 3 , respectively.

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“…On the one hand, the slant wet delay (SWD) was introduced to obtain the troposphere wet refractivity information (Flores et al, 2000;Skone and Hoyle, 2005;Troller et al, 2006;Rohm and Bosy, 2009;Notarpietro et al, 2011;Guo et al, 2016). On the other hand, the slant-integrated water vapor (SIWV) was applied to reconstruct the three-dimensional atmospheric water vapor field (Champollion et al, 2005;Bi et al, 2006;Xia et al, 2013;Jiang et al, 2014;Yao et al, 2016). In our project, we are trying to assimilate the three-dimensional water vapor data into a weather research and forecasting model (WRF), and this paper thus focuses on GNSS water vapor field reconstruction using SIWV data.…”

Section: Theory Of Gnss Troposphere Tomography Modelingmentioning

confidence: 99%

A troposphere tomography method considering the weighting of input information

Zhao

Yao

2017

Ann. Geophys.

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Abstract. Troposphere tomography measurement using a global navigation satellite system (GNSS) generally consists of several types of input information including the observation equation, horizontal constraint equation, vertical constraint equation, and a priori constraint equation. The reasonable weightings of input information are a prerequisite for ensuring the reliability of the adjustment of the parameters. This forms the focus of this research, which tries to determine the weightings, including the observations, for the same type of equation and the optimal weightings for different types of equations. The optimal weightings of the proposed method are realized on the basis of the stable equilibrium relationship between different types of a posteriori unit weight variances, which are capable of adaptively adjusting the weightings for different types of equations and enables the ratio between the two arbitrary a posteriori unit weight variances to tend to unity. A troposphere tomography experiment, which was used to consider these weightings, was implemented using global positioning system (GPS) data from the Hong Kong Satellite Positioning Reference Station Network (SatRef). Numerical results show the applicability and stability of the proposed method for GPS troposphere tomography assessment under different weather conditions. In addition, the root mean square (RMS) error in the water vapor density differences between tomography-radiosonde and tomography-ECMWF (European Centre for Medium-Range Weather Forecasts) are 0.91 and 1.63 g m −3 , respectively, over a 21-day test.

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A method to improve the utilization of GNSS observation for water vapor tomography

Cited by 34 publications

References 29 publications

New parameterized model for GPS water vapor tomography

New parameterized model for GPS water vapor tomography

Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach

A troposphere tomography method considering the weighting of input information

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