## 3 Results and Discussion  
### Seasonal Variation  
In this study, we investigated the impact of seasonal variation on the accuracy of the vertical positioning of GNSS through the analysis of continuous GNSS stations' time series data. It is widely recognized that weather changes and the presence of water vapor in the atmosphere can influence the position accuracy of GNSS, leading to fluctuations in GNSS height measurements. Additionally, the height component is particularly susceptible to variations in air passage. To assess these effects, we conducted time series analysis on the daily coordinate values of the stations. This analysis allowed us to identify statistically significant trends, periodic patterns, and stochastic components in the station data. Our analysis yielded valuable insights, including determining annual vertical velocities and calculating standard deviations associated with these velocities. For the selected stations based on ellipsoid heights, we calculated the velocity and standard deviation values for the height component monthly, seasonally, and yearly. Notably, as the ellipsoid height increased, we observed a decrease in the velocity and its corresponding standard deviation. Specifically, the station with the lowest ellipsoidal height exhibited the minimum vertical velocity values during winter, while the station with the highest ellipsoidal height demonstrated this minimum during autumn. Furthermore, the station with the lowest ellipsoidal height showcased the minimum standard deviation values during winter, whereas the station with the highest ellipsoidal height displayed this minimum during summer. Our findings show that velocity changes resulting from seasonal variations can be significant, particularly in high-precision geodetic surveys. Therefore, it is crucial to account for these effects when conducting such surveys. Figure 6 shows the seasonal, monthly, and annual calculated velocities at the stations. The minimum velocity for RZE1 and DIYB was observed in December during the winter. The minimum velocity for GEME was observed in August in the summer, while it was observed in November for HINI in the autumn. On the other hand, the maximum velocities were observed at stations in different seasons. RZE1, with the lowest ellipsoid height, showed the highest velocity in autumn in September, while HINI, with the highest ellipsoid  
Figure 5: The histograms of ARMA model residuals for RZE1, DIYB, GEME, and HINI stations, respectively. height, showed its maximum velocity in December in winter. Figure 7 shows the stations' seasonal, monthly, and annual calculated standard deviations. The minimum standard deviation was observed at the RZE1 and DIYB stations in the winter. The minimum standard deviation for GEME was observed in the spring and for HINI in the summer. The maximum standard deviation for RZE1, DIYB, and GEME was observed in the summer, while for HINI, this value was observed in the autumn. Furthermore, we calculated the velocity values of the stations for various years and observed a decrease in the height component as the observation duration was extended. With longer observation periods for the height component, both the velocity and their corresponding standard deviation values showed a decreasing trend. It is recommended to have a data length exceeding 4.5 years for accurate velocity estimates, thus minimizing the possibility of velocity estimation errors. Seasonal effects and various meteorological factors such as daily average temperature, pressure, relative humidity, wind speed, and precipitation can affect periodic movements in the altitude component of GNSS stations. Considering the relationship between these  
Figure 6: Vertical velocity variations of GNSS stations. Figure 7: Standard deviation variations for vertical velocities of GNSS stations. factors and the stations' height component, 2014 and 2019 meteorological station data were obtained from the General Directorate of Meteorology.