# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### Europe  
**General**. [PERSON] and [PERSON] [105] used a hybrid approach to time series modeling of GNSS IWV series over four sites in Europe (years 2009-2012, 5-min sampling), combining least squares harmonic estimation (for modeling harmonic characteristics) with least squares support vector machine optimized by cross-validation strategy (for modeling non  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **Year** & **N** & **Start** & **End** & **Region** & **Type** & **Value** \\ \hline
[104] & 2011 & 10 & 2002-01 & 2011-12 & Iberian P. (Spain) & Diurnal & min: 0430–0530 UTC, Afternoon \\ \hline
[105] & 2015 & 4 & 2009-01 & 2012-12 & Europe & Seasonal & \\ \cline{3-6} \cline{6-6  
harmonic features). Inter-seasonal variability was found to be greater than inter-annual variability. Diurnal cycles were strong in summer with maxima between 1200 and 1800 LST. The peaks were related to solar flux and the phase to other features, such as location or wind/temperature patterns. **Northern Europe**. [PERSON] et al. [100] studied trends of water vapor in the region of Scandinavia during the years 1993 and 2000. They found that the GNSS station radome can affect measurements to an extent between 0.0 and 1.8 mm. Trends were positive, 0.1-0.2 mm year\({}^{-1}\). These GNSS-calculated trends were found to be consistent with other measurements (i.e., radiosondes and MWR), assessing the potential of GNSS IWV in climate monitoring. [PERSON] et al. [102] analyzed the diurnal variability of water vapor in 32 sites in the Baltic region, during 1996-2005. Peak to peak difference was of 0.5-0.6 mm in spring/summer, while winter and autumn lacked a clear pattern. The high variability (mainly with synoptic situation, or substitution of air masses) makes that the diurnal cycle can only be observed when averaging over many years. **France**. [PERSON] et al. [108] combined different types of water vapor measurements to study two sites in France. The work presented a non-significant trend at the site of Cezeaux of \(0.42\pm 0.45\) mm decade\({}^{-1}\). Both stations presented almost no diurnal cycle and the authors concluded that variability of surface water vapor was influenced by sporadic circulation patterns rather than by regular diurnal variations. **Switzerland**. [PERSON] et al. [110] studied trends of water vapor over Switzerland using different instruments, including 31 GNSS sites in the period 1995 to 2018. Jumps caused by instrumentation changes were corrected. IWV was found to increase with a rate of (2-5% decade\({}^{-1}\)). From the comparison among different instruments, it was concluded that GNSS data are reliable for the detection of trends, but a few stations need quality control and harmonization. **Spain**. [PERSON] et al. [104] analyzed the diurnal cycle of IWV in 10 GNSS Spanish stations for the years 2002-2011, finding that it explained 3-7% of sub-daily annual variability. IWV was found to peak at 0430-0530 UTC, and the diurnal cycle was found to be more important in the Mediterranean sites than in the inland or Atlantic ones. The summer diurnal cycle is also more variable among sites. The first two harmonics (diurnal and semidiurnal cycles) explained 97% of the variance. Another work analyzed the annual cycle in the same period and stations [114], finding that the 12-month harmonic explained 96% of variance, with a maximum in summer and minimum in the winter months. Southwestern sites, however, showed a local minimum in July, which can be explained by the presence of dry air masses. The Mediterranean sites had larger amplitudes than inland sites. The July local minimum had been already reported by [PERSON] et al. [115], who characterized the IWV in the Gulf of Cadiz region, in southern Iberian Peninsula using GNSS, radiosondes and a sun-photometer. **Germany**. [PERSON] et al. [106] studied the IWV time series over 113 GNSS stations in Germany, with 10-19 years of temporal coverage, together with meteorological data from automatic stations and ERA-Interim reanalysis.