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.
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.
Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.
Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (), the ZHD and deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3- during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–2020 period. Furthermore, the precisions of improved and precipitable water vapor (PWV) were verified by radiosonde-derived and PWV in the 2016–2019 period. Compared with the IMM-ZHD and GNSS-PWV products, the mean Bias and RMS of GPT3-ZHD are −0.5 mm and 2.1 mm, while those of GPT3-PWV are 2.7 mm and 11.1 mm. Compared to the radiosonde-derived , the mean Bias and RMS of GPT3- are −0.8 K and 3.2 K. The mean Bias and RMS of the improved-ZHD model from 2019 to 2020 are −0.1 mm and 0.5 mm, respectively, decreasing by 0.4 mm and 1.6 mm compared to the GPT3-ZHD, while those of the improved- are −0.6 K and 2.7 K, respectively, decreasing by 0.2 K and 0.5 K compared to GPT3-. The mean Bias and RMS of PWV calculated by GNSS-ZTD, improved-ZHD, and improved- are 0.5 mm and 0.6 mm, respectively, compared to the GNSS-PWV, decreasing by 2.2 mm and 10.5 mm compared to the GPT3-PWV. It indicates that the improved ZHD and models can be used to obtain the high-precision PWV. It can be applied effectively in the retrieval of high-precision PWV in real-time in the Yangtze River Delta region.
Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones’ paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events.
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