Extending a model for water vapor sounding by ground-based GNSS in the vertical direction

Yao, Yibin; Sun, Zhangyu; Xu, Chaoqian; Xu, Xingyu; Kong, Jian

doi:10.1016/j.jastp.2018.08.016

Cited by 11 publications

(14 citation statements)

References 20 publications

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“…As for T m , we adopt the linear correction method (Yao et al, ; Zhang et al, ):

T_{m} = T_{m 0} - β ((), h - h_{0})

where T m 0 is the weighted mean temperature (K) at h 0 (km) and β is the lapse rate (K/km).…”

Section: Methodsmentioning

confidence: 99%

“…Yao et al (2012) proposed the Global Weighted Mean Temperature model which provides accurate empirical T m worldwide and was updated and perfected in 2013 and 2014 (Yao et al, 2013. Other similar models are also established, including Chen et al (2014), He et al (2017), Zhang et al (2017), Ding (2018), Yao et al (2018), and Huang et al (2018). In addition, some tropospheric delay models, such as the GPT2w model (Böhm et al, 2015), the TropGrid2 model (Schüler, 2014), and the ITG model (Yao et al, 2015), also provide empirical T m .…”

Section: Introductionmentioning

confidence: 99%

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An ERA5‐Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions

Sun

Bao

Yao

2019

Earth and Space Science

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Tropospheric delay is an important error source in Global Navigation Satellite System (GNSS) positioning and can also be used in water vapor monitoring. Many models have been built to correct tropospheric delays or to convert zenith wet delays to precipitable water vapor. However, these models suffer from limited resolutions (spatial resolution lower than 1°and temporal resolution lower than 6 hr), which affects their performance. The release of European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) provides the opportunity to lift this limit. In this study, we use the ERA5 hourly 0.5°× 0.5°data to build a new model over China, which integrates tropospheric delay correction for GNSS positioning and weighted mean temperature calculation for GNSS meteorology. By modeling the diurnal variations of zenith hydrostatic delay, zenith wet delay, and weighted mean temperature and the seasonal variations in their lapse rates, this model has the state-of-the-art spatial resolution of 0.5°× 0.5°and temporal resolution of 1 hr. We validate this new model by the ERA5 data, the radiosonde data, and the GNSS data in comparison with the canonical GPT2w model. The results show that the new model has better accuracies in terms of root-mean-square than the GPT2w model in all parameters. Especially, the new model well captures the diurnal variations in tropospheric delay and weighted mean temperature. This new model provides accurate tropospheric delays and weighted mean temperature simultaneously, which enables GNSS receivers to measure precipitable water vapor directly and also benefits GNSS positioning.

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“…As for T m , we adopt the linear correction method (Yao et al, ; Zhang et al, ):

T_{m} = T_{m 0} - β ((), h - h_{0})

where T m 0 is the weighted mean temperature (K) at h 0 (km) and β is the lapse rate (K/km).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An ERA5‐Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions

Sun

Bao

Yao

2019

Earth and Space Science

Self Cite

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“…As for Tm, we adopted the linear correction method that uses a linear lapse rate [24,26,28]: Residual (mm) Figure 4e-h shows that the ZHD fitting residuals from Equation (6) are overall smaller than those from Equation (5) at these four grid points. Figure 5e-h shows that the ZWD fitting residuals from Equation (6) are overall smaller than those from Equation (5) at the grid point 60 • N and 115 • E, and are similar at the other three grid points.…”

Section: Height Corrections For Tropospheric Delay and T Mmentioning

confidence: 99%

“…Literature study [21] constructed the empirical Global Weighted Mean Temperature (GWMT) model, which contributed to the real-time calculation of PWV. Other T m models have also been established [22][23][24][25][26][27][28], which has accelerated the development of GNSS meteorology.…”

Section: Introductionmentioning

confidence: 99%

A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

2019

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Precise modeling of tropospheric delay and weighted mean temperature (T m ) is critical for Global Navigation Satellite System (GNSS) positioning and meteorology. However, the model data in previous models cover a limited time span, which limits the accuracy of these models. Besides, the vertical variations of tropospheric delay and T m are not perfectly modeled in previous studies, which affects the performance of height corrections. In this study, we used the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis from 1979 to 2017 to build a new empirical model. We first carefully modeled the lapse rates of tropospheric delay and T m . Then we considered the temporal variations by linear trends, annual, and semi-annual variations and the spatial variations by grids. This new model can provide zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and T m worldwide with a spatial resolution of 1 • × 1 • . We used the ECMWF ERA-Interim data and the radiosonde data in 2018 to validate this new model in comparison with the canonical GPT2w model. The results show that the new model has higher accuracies than the GPT2w model in all parameters. Particularly, this new model largely improves the accuracy in estimating ZHD and T m at high-altitude (relative to the grid point height) regions.

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“…In the early stage, the spherical harmonics function is usually employed to establish tropospheric correction models [9][10][11][12][13]. Recently, tropospheric correction models were constructed by storing model coefficients in a grid form with different spatial resolutions, which can account for both latitudinal and longitudinal variations [14][15][16][17]. The grid empirical model has been demonstrated with high accuracy and robustness, and the GPT2w model is one of the most widely used grid empirical models [18].…”

Section: Introductionmentioning

confidence: 99%

Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS)

Yao

Sun

2018

Remote Sensing

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With the availability to high-accuracy a priori zenith wet delay (ZWD) data, the positioning efficiency of the precise point positioning (PPP) processing can be effectively improved, including accelerating the convergence time and improving the positioning precision, in ground-based Global Navigation Satellite System (GNSS) technology. Considering the limitations existing in the state-of-the-art ZWD models, this paper established and evaluated a new in-situ meteorological observation-based grid model for estimating ZWD named GridZWD using the radiosonde data and the European Centre for Medium-Range Weather Forecasts (ECWMF) data. The results show that ZWD has a strong correlation with the meteorological parameter water vapor pressure in continental and high-latitude regions. The root of mean square error (RMS) of 24.6 mm and 36.0 mm are achievable by the GridZWD model when evaluated with the ECWMF data and the radiosonde data, respectively. An accuracy improvement of approximately 10%~30% compared with the state-of-the-art models (e.g., the Saastamoinen, Hopfield and GPT2w models) can be found for the new built model.

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Extending a model for water vapor sounding by ground-based GNSS in the vertical direction

Cited by 11 publications

References 20 publications

An ERA5‐Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions

An ERA5‐Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions

A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS)

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