A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography

Yang, Fei; Guo, Jiming; Shi, Junbo; Zhou, Lv; Xu, Yi; Ming, Chen

doi:10.3390/s18082526

Cited by 17 publications

(17 citation statements)

References 37 publications

(34 reference statements)

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“…In the research area, the water vapor density, x i , within a certain voxel can be represented by the weighted average of its neighbors because of the relatively stable horizontal distribution [30,31]. The specific horizontal constraint equation is as follows:…”

Section: Constraint Equationsmentioning

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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Section: Constraint Equationsmentioning

confidence: 99%

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

et al. 2019

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“…In GNSS data processing, the PWV is translated from zenith wet delay (ZWD) by the following formula [21,22]:…”

Section: Pwv Derived From Gnss and The Gpt2w Modelmentioning

confidence: 99%

Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

et al. 2019

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With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days.

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“…The Saastamoinen model with a global mean bias/RMS of 1.01/16.9 mm achieved the best ZHD estimated values compared with the other two ZHD models.ZHD is usually needed [4,5]. In addition, accurate ZHD is a prerequisite for obtaining water vapor information in GNSS meteorology, as ZWD is achieved by subtracting ZHD from ZTD [6][7][8][9][10][11].ZHD can be derived with millimeter-level accuracy from meteorological parameters and position at an observation station using a model, such as the Saastamoinen, Hopfield, and Black models [12][13][14]. However, most GNSS sites are not equipped with meteorological sensors, and there are often no collocated weather stations available for those GNSS sites.…”

mentioning

confidence: 99%

“…ZHD is usually needed [4,5]. In addition, accurate ZHD is a prerequisite for obtaining water vapor information in GNSS meteorology, as ZWD is achieved by subtracting ZHD from ZTD [6][7][8][9][10][11].…”

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confidence: 99%

The Influence of Different Modelling Factors on Global Temperature and Pressure Models and Their Performance in Different Zenith Hydrostatic Delay (ZHD) Models

et al. 2019

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Surface temperature and pressure are indispensable variables in Global Navigation Satellite System (GNSS) meteorology. The lack of meteorological observations located at or near the GNSS sites is a big challenge for the calculation of accurate zenith hydrostatic delay (ZHD). Therefore, various empirical models with different model forms were established to provide temperature and pressure values. In this study, the influence of different modelling factors, including model forms, temporal resolution of the data sources, and the spatial resolution of the data sources, is evaluated and the temperature and pressure model with the best performance is developed. On the basis of the meteorological parameters estimated by the above model, we analyzed the global performance of the three most commonly used ZHD models, that is, the Saastamoinen, Hopfield, and Black models. The numerical results show that the model with the idea of time-segmented modelling performs best, of which the global mean root mean square (RMS), mean absolute error (MAE), and standard deviation (SD) are 7.87/6.33/7.17 hPa and 2.95/2.31/2.79 K for pressure and temperature, respectively, using the data sources with temporal resolution of 2 h and spatial resolution of 2.5 • × 2 • in the reanalysis data comparison. In comparison with the radiosonde data, the mean RMS/MAE/SD are 7.02/5.24/6.46 hPa and 4.05/3.17/3.86 K for pressure and temperature, respectively. The Saastamoinen model with a global mean bias/RMS of 1.01/16.9 mm achieved the best ZHD estimated values compared with the other two ZHD models.ZHD is usually needed [4,5]. In addition, accurate ZHD is a prerequisite for obtaining water vapor information in GNSS meteorology, as ZWD is achieved by subtracting ZHD from ZTD [6][7][8][9][10][11].ZHD can be derived with millimeter-level accuracy from meteorological parameters and position at an observation station using a model, such as the Saastamoinen, Hopfield, and Black models [12][13][14]. However, most GNSS sites are not equipped with meteorological sensors, and there are often no collocated weather stations available for those GNSS sites. Thus, a standard model for providing precise and unbiased global pressure and temperature values is often used, for example, the global pressure and temperature (GPT) series models, TropGrid2 model, and the improved tropospheric grid (ITG) model [15][16][17][18][19].The GPT model is proposed by Boehm et al. [15], which is developed from three-year European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA)-40 products, and provides temperature and pressure based on spherical harmonics up to degree and order 9, leading to coarse horizontal resolution of about 20º. A more precise ECMWF ERA-interim product was applied by Lagler et al. to establish the GPT2 model [16,20], in which the semi-annual harmonics was incorporated to better account for regions where very rainy periods or very dry periods dominate, making it an improved model to provide meteorological parameters resting upon a global 5º grid ...

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A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography

Cited by 17 publications

References 37 publications

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

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

Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

The Influence of Different Modelling Factors on Global Temperature and Pressure Models and Their Performance in Different Zenith Hydrostatic Delay (ZHD) Models

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