The development of the International Global Navigation Satellite System (GNSS) Service (IGS) Real-Time Pilot Project shows promise for real-time GNSS-based precipitable water vapor (GNSS-PWV) retrieval. For better applications of real-time GNSS meteorology over China, a method is proposed to establish a real-time GNSS-PWV monitoring system using the national GNSS network of China. The prototype system generating the zenith tropospheric delay (ZTD) is based on the real-time precise point positioning technique, in which the real-time data streams and state-space-representative satellite orbit and clock corrections are processed. The parallel computing technology is embedded in the system for decoding and processing the real-time data streams, which enables the multistation processing mode and improves the computing efficiency. At the initial phase, a total of 215 global positioning system (GPS) stations from the crustal movement observation network of China are used to generate the real-time GPS-ZTDs. The pressure data for separating the zenith wet delay (ZWD) from the ZTD are obtained from the collocated meteorological sensors. An advanced weighted mean temperature model, namely, Gridded-Mixed Tm, is adopted in the system to determine the conversion factor “Π” (converting GPS-ZWD to GPS-PWV) in real-time mode with the measured temperatures input. The generated real-time GPS-PWV products have a time resolution of 5 min. To validate the system performance, comparisons between the real-time GPS-PWV and the PWVs derived from nearby radiosonde data (RS-PWV) and reanalysis data from the National Centers for Environmental Prediction (NCEP)-Department of Energy Reanalysis II (NCEP-II-PWV) are conducted over a period of 83 days. The comparisons show a mean bias of −0.1 mm with a root mean square (RMS) of 1.7 mm between the real-time GPS-PWV and RS-PWV. The agreement between the real-time GPS-PWV and NCEP-II-PWV is approximately 2.0 mm in terms of RMS and has a mean bias of −0.8 mm. These results confirm that the established system can be used for real-time PWV monitoring across China.

Global Navigation Satellite System (GNSS) offer all-weather and real-time capabilities, enabling the real-time monitoring of precipitable water vapor (PWV). Traditional method for obtaining high-precision GNSS PWV require meteorological parameters such as pressure and the atmospheric weighted mean temperature, which are inconvenient to obtain at GNSS stations, especially in real-time PWV monitoring. To address these challenges, a deep neural network (DNN)-based model, namely D-PWV, has been established to directly convert zenith total delay (ZTD) derived from GNSS signals into PWV without relying on meteorological parameters. The performance of the D-PWV model is comprehensively assessed using GNSS PWV, the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), radiosonde (RS), and data from the 2018 typhoon event Mangkhut in the Guangxi region of China, respectively. The results reveal that the D-PWV model exhibits a bias of -0.25 mm, 0.09 mm, and 0.28 mm when compared to GNSS PWV, RS PWV, and ERA5 PWV, respectively. The corresponding RMSE values are 2.00 mm, 3.37 mm, and 2.74 mm. Notably, when compared to the backpropagation neural network (BP), the D-PWV model demonstrates a smaller range of variation in both Bias and RMSE across the study area and shows greater stability in the latitudinal direction. Besides, the D-PWV model performs stably during Typhoon Mangkhut, with a correlation coefficient of 0.96 with the RS PWV, and the changes on GNSS stations are also highly consistent with the ERA5 PWV, effectively reflecting the PWV variations in the Guangxi region during the typhoon. It shows that the model constructed in this paper can provide real-time PWV values in the absence of measured meteorological parameters.