## 5 Results and Discussion
### Long Term Analysis  
The analysed series cover a time interval ranging from a maximum of 11 years to a minimum of 6 years (Table 3, Figure 1). The tropospheric time-delay parameters in the zenith direction (ZTD, ZHD, ZWD and PWV) are retrieved from the GAMIT daily solutions [33]. Meteorological data (pressure, temperature and humidity) were extracted from the GPT2 and VMF1 models [26; 27]. There is clear evidence that the time evolution of the delay parameters and PWV are ruled by the evaporation cycle, showing a clear yearly periodicity (Figures 2 and 3), with minimum values reached during January-February and peak values in July-August (Figure 4). Previous studies, e.g., [38], have shown that correlation between the annual cycle of PWV and temperature depends on factors such as the proximity to the coast, the altitude and the wind regime. The study area is very close to the sea; the mere variation in temperature throughout the year is expected to cause the amount of water vapor to be much greater during the warmer months. In fact, in summertime, the sea-breeze regime also increases, bringing humidity over land. [PERSON] et al. (2021) in analyzing the data of 21 GNSS stations in Israel, have shown that the PWV mean annual cycle peaks in September-October and is minimal in February-March. For illustrative purposes only, Figure 2 shows the time series obtained at station AGR1. A periodic trend of the precipitable water values can be clearly seen, which appears clearly dominated by a seasonal character, coherently, with the climate in the region. To better highlight this component, the time series have been filtered with a low-pass FFT FIR filter, with 0.001 cycle/day (cpd) as a cut-off frequency and 0.001 cpd bandwith. Similar results were also obtained by fitting a set of sine waves in the time series. A spectral analysis was performed, confirming the presence of this annual component (frequency = 0.0027 cycles-per-day). Spectral analyses of the annual cycle of precipitable water were performed for five stations distributed at different altitudes and distances from the coast (Figure 3a). Small differences in the spectral amplitudes were observed, varying between 4.5 and 6.5 mm, with the lowest values for the higher altitude stations BKE1 and ENAV. The largest amplitude was recorded at station PACA, which is also the most inland station (about 20 km from the coast). We obtained some results of lesser clarity than [PERSON] et al. [38]. Indeed, all the stations analyzed can be considered coastal; frankly, we did not analyze any continental stations. The effect of altitude therefore seems to be prevailing. The overlap of the amplitudes for ENAV and BKE1 could be explained by the peculiar position of ENAV, which is located on a peninsula of which three sides are exposed to the breeze, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **Altitude** & **Time Span** & **PWV Trend Rate** \\
**(Id)** & **(m a.s.l.)** & **(Years)** & **(mm/Decade)** \\ \hline AGR1 & 70 & 10 & +1.94 \(\pm\) 0.15 \\ BKE1 & 864 & 10 & +1.8 \(\pm\) 0.22 \\ BKNO & 959 & 11 & N.D. \\ ENAV & 485 & 10 & +1.4 \(\pm\) 0.12 \\ MAFE & 57 & 6 & N.D. \\ ONPI & 123 & 10 & +2.0 \(\pm\) 0.22 \\ OSVE & 624 & 6 & N.D. \\ PACA & 74 & 10 & +1.8 \(\pm\) 0.11 \\ PRET & 209 & 11 & +1.1 \(\pm\) 0.26 \\ SANA & 156 & 11 & +1.94 \(\pm\) 0.26 \\ TERZ & 180 & 11 & +2.9 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PWV trends retrieved from the GAMIT/GLOBK analysis [33] applied on the decadal GPS data sets; it was not possible to determine the trend for BKNO because data had different gaps. which could produce a flywheel effect by regulating the seasonal variations of humidity. Spectral analysis on a moving window (spectrogram) clearly shows (Figure 3b) that the amplitude of the annual component can be variable from year to year, as it is linked to the amount of incoming solar radiation and the seasonal average temperatures, which control the seasonal cycles of evaporation and evapotranspiration.