###### Abstract  
Estimating the height component of Global Navigation Satellite System (GNSS) stations is widely known to be more challenging than estimating the horizontal position. In this study, we utilized height time series data from 37 continuous GNSS stations that were part of the Turkish RTK CORS Network called TUSAGA-Active (Turkish National Permanent GNSS Network Active). The data covered the period from 2014 to 2019, and the selection of stations focused on the Eastern Anatolia region of Turkey due to its topographic characteristics and the pronounced influence of seasonal changes, which facilitated the interpretation of the effects on the height component. The daily coordinates of the GNSS stations were derived using the GAMIT/GLOBK software solution. We identified statistically significant trends, periodic variations, and stochastic components associated with the stations by applying time series analysis to these daily coordinate values. As a result, the vertical velocities of the GNSS stations were determined, along with their corresponding standard deviations. Furthermore, examining the height components of the continuous GNSS stations revealed seasonal effects. We aimed to investigate the potential relationship between these height components and meteorological parameters. The study provides evidence of the interconnectedness between the height components of continuous GNSS stations and various meteorological parameters. Simple linear regression analysis and ARMA time series modeling were utilized to establish this relationship. GNSS height component; GNSS time series; velocity estimation; meteorological parameters; simple linear regression; autoregressive moving average +
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velocities. Also, velocity domains were evaluated by analyzing the noise content's time series type and amplitude. A more accurate estimation of the seasonal signal is required to distinguish signals produced only by tectonic movement from other signals. For this reason, [PERSON] et al. [22] performed seasonal signal analysis and long-term trend analysis in height time series at IGS stations. Strong seasonal variations in amplitudes of 4-9 mm were observed in the height component at the IGS stations. In previous studies of GNSS time series, the seasonal signal was thought to have annual or semi-annual periods, which can be described by a harmonic pattern of constant amplitude and phase [12; 18; 20; 23]. Time series analysis employs various techniques to distinguish between tectonic displacement signals and seasonal signals, which arise from the inherent nature of the signal and other factors. [PERSON] et al. [15] proposed a linear Kalman filter model to explain the behavior of seasonal signals, assuming that the stochastic model includes random noise. However, this hypothesis is implausible, as numerous studies have demonstrated that GNSS time series demonstrate flicker noise. Specifically, GNSS station velocities are often estimated by considering the presence of white, flicker, and random walk noise [12; 20; 24]. Significant semi-annual and annual variations are visible in the height time series created using continuous GNSS station data over several years [25]. Seasonal movement observed in continuous GNSS time series can be a source of error in coordinate time series and thus needs to be modeled to improve positioning accuracy. One of the important parameters affecting the accuracy of the velocity components at each station is the observation (session) time. The purpose of using different observation times is to increase station position accuracy. In order to minimize seasonal effects, it is recommended to estimate velocities with the help of at least 3 years of observations [26; 27]. [PERSON] et al. [28] examined the relationship between climate change and the height component of GNSS stations with the data for July and December from GNSS stations that measure continuously from different networks in Taiwan.