# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 3 Inter-Comparisons with Other Techniques  
### Validations of GNSS Water Vapor with Other Techniques as Reference  
Table 1 presents a summary of the main findings of the works used to develop this subsection. One of the first GNSS IWV validations as an emerging system for water vapor monitoring was performed by [PERSON] et al. [50]. They applied an approach that incorporated GNSS stations that were far away (long baselines) to estimate the absolute ZTD, while previous methodologies used WVR for calibration of the relative ZTD path. This could lead to errors, since WVR overestimate IWV during rainfall. Then, they compared IWV at a few stations in Oklahoma and Kansas with nearby WVR stations, obtaining negligible bias and RMSE in the interval 1.0-1.5 mm. It was proved that GNSS could, by itself, provide accurate ZTD values without the need of auxiliary instruments, which was specially important for all-weather IWV retrieval, since WVR typically fails under intense rainfall. Another work analyzed the usefulness of GNSS for IWV monitoring, and compared the estimates against NWP. It was found that, although GNSS IWV showed the potential to become a powerful tool for improving forecasts, the products still needed to be refined, as biases were found in the 2-4 mm and RMSE of 3.8 mm [51]. In this work, it must be noted that the meteorological data used for converting ZTD to IWV was obtained from the NWP itself, and therefore not as accurate as a meteorological station, specially for near real-time applications. There were also validations against radiosonde stations, which are usually a reference for water vapor sensing. For instance, [PERSON] et al. [52] studied the comparison of GNSS and WVR against radiosondes during 1999 in non-precipitating conditions, using the Cagliari (Italy) GNSS station and nearby radiosonde and WVR sites. The bias between  
GNSS and radiosondes was 0.4 mm and the standard deviation 1.9 mm, which already showed better results than [PERSON] et al. [51]. It is useful to test the slant-IWV, as it can be used in some applications (for instance, tomography) and it is also an interesting exercise to evaluate GNSS observations without the possible errors from mapping functions. [PERSON] et al. [53] compared line-of-sight integrals of water vapor from GNSS using WVR as a reference in Oklahoma. They found very good agreement between both techniques (correlation 0.99, RMSE 1.3 mm). This work concluded that GNSS data provided an all-weather alternative that is especially useful under rainfall or sever weather events, situations in which other instruments (such as WVR itself) fail. The potential of GNSS IWV for model verification was assessed by [PERSON] et al. [60]. GNSS was starting to be acknowledged as a reliable technique for validating models and other instruments. Particularly, this work includes some validation of GNSS IWV against radiosondes and MWR in Switzerland between 2001 and June 2003. During daytime, GNSS showed a positive bias against radiosonde (0.9 mm), while at night, the bias became negative (\(-0.4\) mm). However, against MWR, both right and daytime GNSS IWV showed positive bias (0.3 mm). Therefore, in general terms, it was concluded that GNSS IWV was an accurate technique, and it was used to evaluate Alpine Model analysis and forecast [60]. The model showed a seasonal dependence with an increased dry bias in summer 2002. The main conclusion was that GNSS provide information accurate enough for model verification and assimilation. Another work [54] compared the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) forecast IWV against GNSS, radiosonde and microwave radiometer (MWR), showing that summer had worse performance (higher errors, smaller correlation) than other seasons. In this work, summer also had a higher bias (wet in this case) but this is associated to the fact that IWV values are higher in summer. The diurnal cycle is also reported to be an aspect to improve, and errors are mainly associated to errors in the temperature daily cycle by MM5.