## 3 Results
### Real-Time Static PPP Accuracy Analysis  
In the process of PPP, if there is no perceptible change in the position of the point to be determined, it can be ignored for a period. Therefore, in data processing, the coordinates of the point to be determined can be considered as a fixed set of constants, called static PPP; otherwise, they are referred to as kinematic PPP. Since the coordinates of the point to be determined can be considered as a set of constants, the positioning accuracy of static PPP is higher than that of kinematic PPP. Therefore, static PPP was used in this paper Since HKSL and HKWS stations in Hong Kong are IGS stations, the station coordinates products published by IGS were used as reference truth values, and HKSL and HKWS stations were used as examples to evaluate their positioning accuracy. When the absolute values of the three directions of E, N, and U were less than 10 cm and were less than 10 cm in the subsequent 20 consecutive epochs, the direction was considered to converge. The positioning error after convergence was calculated to evaluate the accuracy of the real-time PPP static solution. Figure 3a shows the error distribution of real-time GPS PPP at HKSL and HKWS stations. Table 3 shows the RMS and average convergence time of the real-time GPS PPP error at each station. According to Figure 3a, the positioning accuracy of HKSL and HKWS in the U direction of DOY 175 during the rainfall period was about 6 cm, and the positioning accuracy of E, N, and U directions for other days was better than 5 cm. The overall positioning error of the two stations was relatively stable with time. Errors in the E direction of DOY 173 and the U direction of DOY 175 fluctuated greatly, which may have been caused by the unstable atmospheric environment during rainfall. During the nonrainfall period, except for the large fluctuation in the U direction in DOY 231, the overall positioning errors in the E, N, and U directions were relatively stable. Table 3 shows the RMS and convergence time of HKSL and HKWS during rainfall and nonrainfall periods. The results show that the two stations had larger RMS values and longer convergence timesduring rainfall. During rainfall periods with large positioning errors and convergence times, the average positioning accuracy of real-time static GPS PPP in the E, N, and U directions was 1.69 cm, 0.80 cm, and 2.37 cm, respectively, and the average convergence time was 62.50 min. This shows that the convergent GPS PPP can maintain high positioning accuracy and reliability in the three directions of E, N, and U, and the disadvantage is that the convergence time is longer. To study the changes in real-time GPS PPP accuracy and convergence time after adding GLONASS, BDS, and Galileo observations, in this section, real-time CNES products are also used for real-time multisystem (GPS/GLONASS/BDS/Galileo) PPP data processing. Figure 3b shows the error distribution of real-time multisystem PPP in each station after convergence. Table 4 shows the RMS and average convergence time of the real-time multisystem PPP error of each station. As can be seen from Figure 3b, the overall accuracy of the two stations in the E and N directions was improved, and the error of the HKWS station in the E direction in DOY 174 was reduced from 3.25 cm to 1.49 cm. The statistical results in Table 4 show that the average positioning accuracy of the real-time static multisystem combination in the E, N, and U directions during rainfall was 1.48 cm, 0.7 cm, and 2.36 cm, respectively, and the average convergence time was 44.25 min. Compared with real-time GPS PPP, the positioning accuracy of real-time multisystem PPP in the E direction and the N direction was improved by about 11.38% and 5.43%, respectively, and the accuracy in the U direction was maintained. Compared with GPS, the convergence time of real-time multi-GNSS PPP was reduced by 29.20% and 12.06% during rainfall and nonrainfall periods, respectively.