Remote sensing of atmospheric water vapor variation from GPS measurements during a severe weather event

Song, Dong-Seob; Grejner‐Brzezinska, Dorota A.

doi:10.1186/bf03352964

Cited by 31 publications

(21 citation statements)

References 18 publications

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“…Although the zenith tropospheric delay (ZTD) estimated from the GNSS measurements can be directly assimilated into numerical weather prediction (NWP) (Poli et al 2007;Bennitt and Jupp 2012;De Haan 2013), GNSSderived PWV has the potential to be used for the studies of severe weather (Iwabuchi et al 2006;Yeung et al 2009;Li et al 2012) and climate changes (Kruczyk 2015;Bianchi et al 2016;Yeh et al 2016). Previous studies (Song and Grejner-Brzezinska 2009;Li et al 2012;Wang et al 2015) have shown that most severe rainfall events occur in the descending trends of time series PWV over a station after a long ascending period. Moreover, it is suggested by Benevides et al (2015) that the reliability and accuracy of severe weather event forecast could be improved by the analysis of 2D or 3D variation of PWV fields with the use of other meteorological data (Yeung et al 2009;Shi et al 2015;Jiang et al 2016).…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…The methods of estimating the variation of water vapor in the atmosphere will be illustrated and evaluate using Just Integrated Water Vapor model (IWV) with no underline. With In some researches IWV is known as precipitable water vapor (PWV), [1]. If all the water in that column were precipitated as rain.…”

Section: Introductionmentioning

confidence: 99%

Estimation of The Zenith Path Delay using RTK-DGPS measurements

2018

ijs

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In this paper, the method of estimating the variation of Zenith Path Delay (ZPD) estimation method will be illustrate and evaluate using Real Time Kinematic Differential Global Positioning System (RTK-DGPS). The GPS provides a relative method to remotely sense atmospheric water vapor in any weather condition. The GPS signal delay in the atmosphere can be expressed as ZPD. In order to evaluate the results, four points had been chosen in the university of Baghdad campus to be rover ones, with a fixed Base point. For each rover position a 155 day of coordinates measurements was collected to overcome the results. Many models and mathematic calculations were used to extract the ZPD using the Matlab environment. The result shows that the ZPD values in mm depend on the water vapor intensity in the atmosphere and the mean temperatures. Also, the mean temperature model can be generated to calculate the Water Vapor Intensity(WVI (for 20 km highest.

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“…PWV can be converted from the ZWD together with other atmospheric variables. Although the GNSS-derived ZTD can be directly assimilated into numerical weather prediction (NWP) [4,5], PWV has the potential to be used for the studies of severe weather [11][12][13][14] and climate changes [15,16]. Previous studies [13,14,17] have shown that most severe rainfall events occurred in the descending trends of time series PWV over a station after a long ascending period.…”

Section: Introductionmentioning

confidence: 99%

“…It is likely to rain after a steep ascent and sudden descent in PWV. Moreover, Benevides et al [18] suggested that the reliability and accuracy of severe weather forecast could be improved by analyzing 2D or 3D variation in PWV fields with the aid of other meteorological data [13,[18][19][20].…”

Section: Introductionmentioning

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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Remote sensing of atmospheric water vapor variation from GPS measurements during a severe weather event

Cited by 31 publications

References 18 publications

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

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

Estimation of The Zenith Path Delay using RTK-DGPS measurements

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

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