## 4 Assessment of New Method Compared to the Existing E-Gvap Processing  
The new strategy has been initially developed and assessed using the GNSS4 SWEC Benchmark dataset [31] and firstly compared to the GOP NRT tropospheric solution contributing operationally to E-Gvap. Though the new strategy can provide RT and NRT products in high temporal resolution, we compared only the ZTD as a product of HH:00 and HH:59 time stamps in every hour representing the standard NRT E-Gvap product, see Figure 7.  
Table 4 summarizes results of three strategies and six ZTD solutions using 13 EUREF stations selected from the benchmark campaign and exploiting the EUREF combined ZTD product as a reference for all comparisons [39]. The table show summary statistics indicating similar improvements in terms of the standard deviation over all ZTDs estimated at HH:00 in NRT independently of applied products (IGS RTS vs. IGS final products), processing strategies and software (G-Nut/Tefnut PPP  
Figure 11: Elevation-dependent absolute (**a**) and normalized (**b**) statistics for GOP slant delay differences at POTM and POTS stations using post-processing (PP) product and Kalman filter (F) or backward smoothing (S), GFZ slant delays and old GOP product (GOP_S). Line types indicates model SD (solid), clean SD (dashed), raw SD (dotted).  
Figure 10: Elevation-dependent absolute (**a**) and normalized (**b**) statistics for slant delay differences at POTM and POTS stations using real-time (RT), post-processing (PP) product, Kalman filter (F), backward smoothing (S). Line types indicates model SD (solid), clean SD (dashed), raw SD (dotted).  
vs. Bernese V52 DD). Compared to the Kalman filter ZTD estimated at the last epoch (HH:59), the backward smoothing running on hourly basis showed the improvement of 20% and 24% for the IGS RTS and the IGS final product, respectively. The E-GVAP/GOP product demonstrates a similar improvement (24%) comparing ZTD from HH:00 against HH:59, which corresponds to our previous results [29]. The new adaptable PPP solution using the IGS final orbits reached the same accuracy as the E-GVAP/GOP product using the IGS ultra-rapid orbits and NRT DD network solution from the Bernese GNSS Software. On the other hand, the use of IGS RTS products instead of IGS final products in PPP indicates a degradation of 18% in ZTD SDEV and a 2.5 mm bias, which agrees with the summary in Sections 2.2 and 2.3. It should be finally noted, that the E-GVAP/GOP solution and the reference EUREF solution are based on a similar processing strategy and the software, while the new strategy is significantly different.  
Figure 12 shows standard deviations and biases individually for all stations comparing the first ZTDs (HH:00) and the last ZTDs (HH:59). The statistics of ZTDs from the E-GVAP/GOP solution (PPP:DD-ultra) are plotted in red and pink for HH:00 and HH:59, respectively. The results from the new strategy using the PPP with IGS final orbit and clock products (PPP:IGS_final) are shown in dark and light blue and, using IGS RTS (PPP:IGS03) in black and grey. Standard deviations for all the stations show a similar improvement in the ZTD SDEV over all the strategies, software and precise products. However, there is no significant impact of the strategy on systematic errors, and we can observe only a common positive bias attributed to the use of the IGS RTS products.