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The weighted mean temperature (Tm) is a function of atmospheric temperature and vertical humidity profiles. It plays a crucial role in the progress of retrieving water vapor information from the tropospheric delay of GNSS signals. The Tm estimated by the empirical models is always used to convert the zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. However, these empirical Tm models used trigonometric functions, making it difficult to describe Tm in detail and leading to an obvious accuracy difference with latitude changes. Thus, a global latitude zone augmentation mode was adopted for the empirical Tm models; the augmentation coefficients for each latitude zone were obtained by introducing the measured surface temperature and using the least-squares method. Using the Tm data of 2011–2015 derived from radiosonde, the GPT3 model, UNB3m model, and GWTMD model were augmented and analyzed. The results show that all augmentation models can improve the accuracy of the estimated Tm compared with their corresponding original models, and their levels of improvement are different. The three augmentation models achieved an average RMSE of 2.79 K, 3.47 K, and 3.22 K, which correspond to 22%, 49%, and 8% improvement against the GPT3 model, UNB3m model, and GWTMD model. In addition, the comparisons with the Tm linear formula were carried out and showed the superiority of the augmentation models. [ABSTRACT FROM AUTHOR]

Water vapor scale height reflects the vertical distribution of water vapor in the atmosphere, which is a vital parameter in tropospheric zenith wet delay (ZWD) modeling and GNSS meteorology. This study accurately calculated the long-term water vapor scale height using 89 radiosonde stations in China, analyzed the characteristics in temporal and spatial variations and explored the impact factors of the water vapor scale height from the perspective of the climate zones. The numerical results indicate that the water vapor scale height in China exhibits annual and semiannual variations with a rising-then-decreasing trend across the four seasons. A relatively larger value for the water vapor scale height were observed in the subtropical monsoon climate and the temperate continental climate zone, in which the values are greater than 2.35 km, while the values in temperate monsoon climate and the alpine plateau climate zone are relatively small. In addition, the water vapor scale height in different climate zones shows different trends, with a downward trend appeared in the subtropical monsoon climate and the alpine plateau climate zone, while an upward trend was observed in the temperate monsoon climate and the temperate continental climate zone. Furthermore, a positive correlation between water vapor scale height and temperature, pressure, and atmospheric precipitable water was found in China, with a varied degree of correlation in different climate zones. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) plays an important role in retrieving high temporal–spatial resolution precipitable water vapor (PWV) and its applications. The weighted mean temperature (Tm) is a key parameter for the GNSS PWV estimation, which acts as the conversion factor from the zenith wet delay (ZWD) to the PWV. The Tm is determined by the air pressure and water vapor pressure, while it is not available nearby most GNSS stations. The empirical formular is often applied for the GNSS station surface temperature (Ts) but has a lower accuracy. In this paper, the temporal and spatial distribution characteristics of the coefficients of the linear Tm-Ts model are analyzed, and then a piecewise-linear Tm-Ts relationship is established for each GPS station using radiosonde data collected from 2011 to 2019. The Tm accuracy was increased by more than 10% and 20% for 86 and 52 radiosonde stations, respectively. The PWV time series at 377 GNSS stations from the infrastructure construction of national geodetic datum modernization and Crustal Movement Observation Network of China (CMONC) were further obtained from the GPS observations and meteorological data from 2011 to 2019. The PWV accuracy was improved when compared with the Bevis model. Furthermore, the daily and monthly average values, long-term trend, and its change characteristics of the PWV were analyzed using the high-precision inversion model. The results showed that the averaged PWV was higher in Central-Eastern China and Southern China and lower in Northwest China, Northeast China, and North China. The PWV is increasing in most parts of China, while the some PWVs in North China show a downward trend. [ABSTRACT FROM AUTHOR]

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model. [ABSTRACT FROM AUTHOR]