Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m³ and 0.92/−0.08 g/m³, respectively, while the values of the traditional method are 1.33/0.38 g/m³ and 1.59/0.40 g/m³, respectively.

Aiming at the urgent demands on (near) real-time ionosphere products, we study the near real-time (NRT) modeling of the global ionospheric total electron content (TEC) by IGS hourly data and introduce the Kalman filter (KF) to solve the model parameters. The main objective of this article is to propose an adaptive method for determining the KF process noise covariance matrix. This method can reflect the change regularity of spherical harmonic (SH) between epochs and consider the impact of the current ionosphere level on SH. It can adaptively adjust the KF process noise covariance matrix of each epoch to improve the accuracy of NRT global ionosphere maps (GIMs). We analyze the effects of different initial values of the state vector and its covariance matrix on the SH coefficients and propose a method to avoid repeated filter initialization. The results show that for different initial values, the filter can reach the state of convergence within 6 h, but a high-precision initial value can significantly accelerate the KF convergence speed. Compared with Global Navigation Satellite System (GNSS) differential slant TEC (dSTEC) observables, the rms of our NRT products xrtg during quiet and magnetic storms are 1.47 and 1.56 TECU, respectively, larger than the postprocessed GIMs, but significantly smaller than those of real-time GIMs. Compared with Jason VTEC, the results also show that the accuracy of xrtg is even better than European Space Agency (ESA) final products during the magnetic storm.

Apart from the well-known applications like positioning, navigation and timing (PNT), Global Navigation Satellite System (GNSS) has manifested its ability in many other areas that are vital to society largely. With the dense setting of the regional continuously operating reference station (CORS) networks, monitoring the variations in atmospheric water vapour using a GNSS technique has become the focus in the field of GNSS meteorology. Most previous studies mainly concentrate on the analysis of relationship between the two-dimensional (2-d) Precipitable Water Vapour (PWV) and rainfall while the four-dimensional (4-d) variations of atmospheric water vapour derived from the GNSS tomographic technique during rainfall events are rarely discussed. This becomes the focus of this work, which investigates the emerging field of GNSS technology for monitoring changes in atmospheric water vapour during rainfall, especially in the vertical direction. This paper includes an analysis of both 2-d, and 4-d, precipitable water vapour profiles. A period with heavy rainfall events in this study was selected to capture the signature of atmospheric water vapour variation using the ground-based GNSS tomographic technique. GNSS observations from the CORS network of Hong Kong were used. Analysed results of the 2-d PWV/4-d water vapour profiles change during the arrival, occurrence, and depression of heavy rainfall show that: (i) the PWV time series shows an increasing trend before the arrival of heavy rainfall and decreases to its average value after the depression of rainfall; (ii) rainfall leads to an anomalous variation in relative humidity and temperature while their trends are totally opposite and show daily periodicity for periods without rain (this is highly correlated with the changes in solar radiation); (iii) atmospheric water vapour presents unstable conditions with intense vertical convective motion and hydrometeors are formed before the arrival of rainfall while returning to relatively stable conditions during heavy rainfall. This study indicates the potential for using GNSS-derived 2-d PWV and 4-d profiles to monitor spatio-temporal variations in atmospheric water vapour during rainfall, which provides a better understanding of the mechanism of convection and rainfall induced by the extreme weather events.

The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) inversion based on GNSS has become one of the common methods. Research on real-time precise point positioning (RT-PPP)-derived ZTD accuracies of SSR corrections from different Analysis Centers (ACs) is important for earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service Analysis Center (IGS). Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d’Etudes Spatiales (CNE) and Wuhan University (WHU) were evaluated, and the RT positioning performance and ZTD accuracies were analyzed for GPS, Galileo, BDS3. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, BDS3 satellites. The accuracies of GPS/Galileo/BDS3 orbits are best at WHU, CAS, WHU with values of 5.57/5.91/11.77 cm, respectively; the STDs of clock error are all better than 0.22/0.19/0.55 ns, and the RMSEs are better than 0.54/0.32/1.46 ns. CAS has the best Signal-In-Space Ranging Errors (SISRE), followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 minutes and 14.4 minutes, respectively, with 3D positioning accuracy both around 3.3 cm, which is better than CNE and GMV. Among them, WHU-SSR has the higher accuracy of RT-PPP-derived ZTD with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.