## 2 Data and Methodologies
### ZTD and PWV Estimation with PPP  
The PPP technique is used in this study for estimating the ZTD with PPP-B2b, CNES, and GBM products. PPP is a fast and efficient means of estimating geodetic parameters such as coordinates and tropospheric delay. The accuracy of the PPP solution is highly dependent on the quality of the satellite orbit and clock because this information is introduced into the PPP processing, and the errors in this information are not eliminated or accounted for. With the improvement in the quality of the satellite orbit and the clock from IGS [52], PPP has become a very promising method in many applications, including the estimation of ZTD from a large network with hundreds or thousands of stations. The accuracy of the ZTD estimated with the PPP solution has been assessed in many studies. Thanks to the establishment of a real-time service in 2011 by IGS, the retrieval of the real-time ZTD or PWV using PPP became possible. Several studies [29; 30; 31; 33; 35] have demonstrated that the accuracy of the real-time PWV can reach 2\(\sim\)3 mm using PPP with high-accuracy real-time satellite orbit and clock products. A modified version of the RTKLIB software package [54] was utilized in this study for GNSS data processing. This version supports the PPP-B2b stream and incorporates new GNSS processing features, including the GPT3 model, to enhance the accuracy and efficiency of the data analysis. In PPP processing, the tropospheric delays in the slant direction are usually expressed as [55]:  
\[\Delta L=T_{priori}\cdot m_{h}(\epsilon)+T_{remain}\cdot m_{w}(e)+\cdot m_{w}( e)\cdot\text{cot}(e)(G_{N}\text{cos}(\alpha)+G_{E}\text{sin}(\alpha)) \tag{7}\]  
where \(T_{priori}\) represents the a priori zenith hydrostatic delay derived from an empirical model and \(m_{h}\) and \(m_{w}\) are the hydrostatic and wet mapping functions, respectively. \(e\) and \(\alpha\) are the elevation and azimuth, respectively, of the satellites. The remaining tropospheric delay \(T_{remain}\), along with two gradient parameters \(G_{N}\) and \(G_{E}\) were estimated during the PPP process. Figure 2: Distribution of the 32 GNSS stations; 11 stations with co-located radiosondes are marked with red triangles, while the location of BJ01 is marked with a green pentagram. ephemeris. The precise clock offset can be calculated using the clock correction parameter in the PPP-B2b message and the broadcast ephemeris. In this study, the orbit, clock, and DCB performances recovered from PPP-B2b messages were evaluated with products from the MGEX pilot project [77]. Before the comparison between PPP-B2b and GBM products, several inconsistencies between these two sets of products needed to be fixed. For more details on PPP-B2b decoding and the methods used to evaluate the orbit, clock, and DCB, see Appendices A-C.  
Because it takes time to obtain the correction information generated and transmitted from the master control station to users via BDS-3 GEO satellites, latency in the correction message received by the user is inevitable. The results show that the latencies of the PPP-B2b clock offsets received by our receiver at station BJ01 range from 5 s to 9 s for both GPS and BDS-3. These offsets are stable and within the nominal validity of the satellite clock corrections, that is, 12 s. As shown in Table 2, for the GPS satellites, the standard deviation (STD) values ranged from 0.14 ns to 0.20 ns, with a mean value of 0.16 ns; for the BDS-3 medium Earth orbit (MEO) satellites, the STD values ranged between 0.11 ns and 0.17 ns, with a mean value of 0.13 ns. The performances of three inclined geosynchronous orbit (IGSO) satellites, that is, PRN C38-40, were worse than those of the BDS-3 MEO satellites, with a mean STD value of 0.18 ns. The results also indicate that there is an obvious day-to-day variation in the STD values for both GPS and BDS-3. For GPS, the daily STD values vary from 0.05 ns to 0.28 ns, with 83.9% and 35.2% of the values being smaller than 0.2 ns and 0.15 ns, respectively.