A System Developed for Monitoring and Analyzing Dynamic Changes of GNSS Precipitable Water Vapor and Its Application

Li, Li; Yuan, Zhimin; Luo, Ping; Jun, Shen; Long, Sichun; Zhang, Liya; Jiang, Zongli

doi:10.1007/978-3-662-46638-4_10

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…The latest studies (e.g., [19,20]) have shown that GNSS-derived PWV has considerable potential applications in precipitation forecasting or meteorological disaster warning. Furthermore, the analysis of water vapor changing trends or the forecasting of short-term precipitation events requires the transformation of one-dimensional PWV data from observation networks to two-dimensional data through spatial interpolation [21].…”

Section: Introductionmentioning

confidence: 99%

“…The spatial interpolation algorithm consists of showing the dynamic distribution for water vapor within the CORS coverage area by real-time processing the GNSS-derived PWV of each receiver in the CORS network. Li et al [20] developed a real-time monitoring and analytical system for the dynamic spatial and temporal variation characteristics of water vapor. Their system can track the dynamic variation in water vapor content and forecast small- and medium-scale extreme weather.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Liu

Zheng

Zhang

et al. 2019

Sensors

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The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Liu

Zheng

Zhang

et al. 2019

Sensors

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“…The accuracy of GNSS-PPP-derived ZTD can be also at a level of millimetres. As a result, the accuracy of the ZWD, calculated by ZWD = ZTD -ZHD, is also at the level of millimetres (Bevis et al 1992;Rocken et al 1995;Li et al 2015). The ZWD can be converted to PWV by the following formula,…”

Section: From Conversion Of Gnss-zwdmentioning

confidence: 99%

“…The GNSS-ZTD of CZSQ is calculated using the PPP strategy as mentioned in section 2 (Li et al 2012(Li et al , 2015. The ZHD is determined using Eq.…”

Section: Formula Bias Rmsmentioning

confidence: 99%

Modelling of weighted-mean temperature using regional radiosonde observations in Hunan China

Li¹,

Wu²,

Wang³

et al. 2018

Terr. Atmos. Ocean. Sci.

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Precipitable water vapour (PWV) over a ground station can be estimated from the global navigation satellite systems (GNSS) signal's zenith wet delays (ZWD) multiplying by a conversion factor that is a function of weighted-mean temperature (T m). The commonly used Bevis T m model (BTM) may not perform well in some regions due to its use of data from North America in the model development. In this study, radiosonde observations in 2012 from three stations-Changsha, Huaihua, Chenzhou in Hunan province, China-were used to establish a new regional T m model (RTM) based on a numerical integration and the least squares estimation methods. Four seasonal RTMs were also established and assessed for 2012. The RTM-derived T m at the three stations from 2012-2014 were validated by comparing it with radiosondederived T m. Results showed that the accuracy of the yearly RTM was improved by 29% over the BTM, and the bias and root mean square (RMS) of all the four seasonal RTMs were slightly smaller than the yearly RTM, and the accuracy of spring, summer, autumn and winter T m models is improved by 5, 13, 4, and 5% respectively. In addition, the bias and RMS of the differences between the GNSS-PWV resulting from the RTM-derived T m and the radiosonde-PWV were 1.13 and 3.21 mm respectively, which are reduced by 34 and 10% respectively. Thus the seasonal RTMs are recommended for GNSS meteorology for Hunan Province.

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“…The accuracy of surface pressure measured by meteorological sensors is generally 0.2∼0.5 hPa, and the accuracy of the ZHD calculated from the Saastamoinen model can be millimeters. Thus the accuracy of GNSS-PPP-derived ZTD can be also at a level of millimeters, and the accuracy of the ZWD, calculated by ZWD = ZTD − ZHD, is also at millimeters [50].…”

Section: Obtaining Pwv From Gnss-zwdmentioning

confidence: 99%

Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

Wang

et al. 2017

Advances in Meteorology

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In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional Tm models (RTMs) that are a fitting function of multiple meteorological factors (Ts, Es, and Ps). One-factor, two-factor, and three-factor RTMs were assessed by comparing their Tm against the radiosonde-derived Tm (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China.

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A System Developed for Monitoring and Analyzing Dynamic Changes of GNSS Precipitable Water Vapor and Its Application

Cited by 4 publications

References 3 publications

A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Modelling of weighted-mean temperature using regional radiosonde observations in Hunan China

Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

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