## 3 Results and Discussion
### Determination of Noise Patterns  
Noise analysis is needed to reveal the stations' characteristics and investigate the time series more deeply. Refs. [12; 13; 20; 24; 34] emphasized that GNSS time series contain time-independent white noise (WN), time-dependent flicker noise (FN), and random walk noise (RWN). In all these analyses, it was observed that the vertical component had more noise than the horizontal component, and the periodic motion of the vertical component was more pronounced. The most suitable noise model for the height component of the stations was investigated using the CATS (GPS Coordinate Time Series Analysis Software) software [49]. CATS software employs Maximum Likelihood Estimation (MLE) to fit a multi-parameter model to time series data, such as fixed GNSS station coordinates. Through MLE, it becomes feasible to simultaneously estimate the amplitudes of various noise sources and unknown factors (e.g., velocity, periodic signals, and coordinate pulses) [12; 41]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & **R-Squared** & **Standard Error** & **MAE** & **MSE** & **RMSE** & **Height** \\ \hline RZE1 & 0.781 & 0.012 & 0.0880 & 0.01 & 0.1135 & 70.69 \\ DIYB & 0.864 & 0.009 & 0.0943 & 0.01 & 0.1173 & 773.67 \\ GEME & 0.850 & 0.008 & 0.1072 & 0.02 & 0.1371 & 1214.79 \\ HINI & 0.839 & 0.008 & 0.1006 & 0.02 & 0.1302 & 1742.62 \\ \hline
**Pressure** & **R-squared** & **Standard error** & **MAE** & **MSE** & **RMSE** & **Height** \\ \hline RZE1 & 0.845 & 0.019 & 0.1106 & 0.02 & 0.1311 & 70.69 \\ DIYB & 0.801 & 0.014 & 0.0987 & 0.02 & 0.1250 & 773.67 \\ GEME & 0.894 & 0.009 & 0.1122 & 0.02 & 0.1458 & 1214.79 \\ HINI & 0.864 & 0.008 & 0.1004 & 0.02 & 0.1288 & 1742.62 \\ \hline
**Relative** & & & & & & \\
**humidity** & **R-squared** & **Standard error** & **MAE** & **MSE** & **RMSE** & **Height** \\ \hline RZE1 & 0.843 & 0.019 & 0.1087 & 0.02 & 0.1136 & 70.69 \\ DIYB & 0.797 & 0.014 & 0.0946 & 0.01 & 0.1184 & 773.67 \\ GEME & 0.894 & 0.009 & 0.1021 & 0.02 & 0.1338 & 1214.79 \\ HINI & 0.861 & 0.008 & 0.0986 & 0.02 & 0.1248 & 1742.62 \\ \hline
**Wind speed** & **R-squared** & **Standard error** & **MAE** & **MSE** & **RMSE** & **Height** \\ \hline RZE1 & 0.845 & 0.019 & 0.1029 & 0.02 & 0.1267 & 70.69 \\ DIYB & 0.794 & 0.014 & 0.0968 & 0.01 & 0.1223 & 773.67 \\ GEME & 0.893 & 0.009 & 0.1096 & 0.02 & 0.1404 & 1214.79 \\ HINI & 0.861 & 0.008 & 0.0967 & 0.02 & 0.1225 & 1742.62 \\ \hline
**Precipitation** & **R-squared** & **Standard error** & **MAE** & **MSE** & **RMSE** & **Height** \\ \hline RZE1 & 0.847 & 0.019 & 0.1122 & 0.02 & 0.1371 & 70.69 \\ DIYB & 0.796 & 0.014 & 0.0962 & 0.01 & 0.1212 & 773.67 \\ GEME & 0.895 & 0.009 & 0.1095 & 0.02 & 0.1396 & 1214.79 \\ HINI & 0.858 & 0.008 & 0.0952 & 0.01 & 0.1201 & 1742.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simple linear regression results for GNSS stations of different ellipsoidal heights (m). Three noise types and combinations of these noise types were used in the study to reveal the correlated noise models. First, the noise was assumed to be only WN, then a combination of WN + FN and WN + RWN was used. The preferred noise pattern was determined to be one of these three combinations. In the second stage, the amplitudes and spectral indices of the noise model were presented simultaneously with the WN. As a result of the analysis, the most appropriate noise model for the height component is given in Table 3. When the MLE values were examined, it was seen that the WN + FN model was the most dominant model among the three noise models, as suggested by [12; 20; 24]. Again, the WN + FN noise model was more suitable for all stations than the WN + RWN noise model [50]. In addition, when the results were examined, the values of WN + FN and WN + RWN noise combinations for all stations were quite close. Amplitudes are one of the important parameters for noise analysis. The amplitudes indicate the magnitude of the appropriate noise pattern present in the available data.