## 4 Conclusions  
This paper aimed to reveal the relationship between the height component of the GNSS stations that make continuous observations and seasonal variation and meteorological parameters. For the vertical displacement analysis of the region, we selected 37 GNSS stations and processed approximately 6 years of continuous data. First, we organized the data and used MATLAB to conduct an outlier test. This test helped identify and handle data points that significantly deviated from expected patterns or exhibited unusual behavior. After editing the data, we plotted the time series and investigated anomalies. We examined the seasonal behavior of GNSS stations and found strong seasonal signals at all stations. Additionally, we observed that GNSS signals exhibited noise. To perform spectral analysis, we utilized CATS software in this study. The most appropriate noise model for the stations was the WN+FN noise model, as expected. We then investigated the relationship between seasonal variation and the vertical time series. Based on our analyses, we calculated the seasonal and annual vertical velocities for each station, along with their respective standard deviations. The results showed variations among stations with different ellipsoid heights. As anticipated, the velocity and standard deviation values improved with longer observation periods. Furthermore, we observed seasonal effects in the height components of continuous GNSS stations, prompting us to investigate their relationship with meteorological parameters. A simple linear regression analysis was conducted to assess this dependency, examining the relationship between the height components of continuous GNSS stations and meteorological factors such as temperature, pressure, relative humidity, wind speed, and precipitation. The analysis revealed a significant correlation between the height components and these meteorological parameters. When the results were examined, it can be seen that the model's performance increased for all parameters as the height of the ellipsoid increased. When the R-squared values approached one, and the MAE and RMSE values approached zero, the model performed successfully in all parameters. In addition to simple linear regression, we employed ARMA models. The ARMA modeling results further confirmed the dependence of the height components on meteorological parameters. The ARMA model results generally aligned well with the time series of all stations, with RMS values typically below 5 mm in the vertical direction, except for the ARTV, ERZ2, GIRS, MUUS, and SIRT1 stations. The results from our study, including the simple linear regression analysis and ARMA modeling, provide strong evidence supporting the association between the height components of continuous GNSS stations and various meteorological parameters. Based on the analyses above, significant improvements in the quality of velocity estimates for continuous GNSS stations, particularly in the vertical component, can be achieved by either working with meteorological parameters or selecting the appropriate season (time) for conducting measurements based on the study's objective. Conceptualization, N.T.U. and U.D. ; methodology, N.T.U. and U.D. ; software, N.T.U. ; investigation, N.T.U. ; resources, N.T.U. ; writing--original draft preparation, N.T.U. ; writing--review and editing, N.T.U. and U.D. ; visualization, N.T.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. All the results obtained at the end of our research have been thoroughly presented in the paper. The data presented in this study are available on request from the corresponding author. In this paper, the figures with geographical maps were generated with the Generic Mapping Tools (GMT) [52] and all the other figures and code were generated with MATLAB and Python.