# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 3 Inter-Comparisons with Other Techniques
### Validations of NWP with GNSS Water Vapor as Reference  
Table 2 presents a summary of the main findings of the works used to develop this subsection. GNSS ground-based stations have been widely used for testing NWP. One of the first works of this kind validated High Resolution Limited Area Model (HIRLAM) against GNSS IWV in Spanish (Madrid Sierra) station [67], obtaining a bias of \(-\)0.4 mm and RMSE of 2.0 mm during two short periods in December 1996. Another work by [PERSON] [68] compared the Baltic Sea Experiment (BALTEX) model and the Europa model against 25 GNSS stations in Finland and Sweden. They found relatively high biases (2.5-3.0 mm), and adequate standard deviations (2.8 mm on average) and correlations (0.92 on average), but with worse results than HIRLAM. Testing against radiosondes yielded smaller biases, what suggested GNSS could have a slight dry bias in that region. SSM/I was also analyzed in this work and showed a good correlation with the GNSS product, assessing the use of both independent instruments for validation and ingestion in analysis. NCEP reanalysis was  
deeply validated at a global scale against GNSS by [PERSON] et al. [69], for a ten-year period (1994-2004) at daily resolution. The study considered 11 clusters of stations for regional analysis. The clusters were based on humidity regimes and geographical information. Also, homogeneity of time-series from GNSS stations was analyzed to select only homogeneous time-series or to correct those which had jumps in the series. The validation of NCEP covered several aspects: correlation, bias, seasonal signal, anomalies and variability. Correlation was shown to be very close to the unit, in the range 0.89-0.99, except in some regions of South America and the Antarctic Peninsula, which are regions that have a small number of observations available to be assimilated by the model and, therefore, worse performance is expected. Regarding bias, most stations were in the \(\pm\)3.0 mm range, and the biases were associated to differences between the model height and the GNSS station height (which causes errors due to the assumption of temperature lapse rate). The biases were also associated with stations with high elevation, where the reduction of pressure to station level could also be error prone. Biases in stations that did not fit with these two problems (i.e., large elevation differences or large elevations) are associated with NCEP problems, such as inconsistencies in assimilated observations and inability of the model to resolve local changes. Regarding the seasonal signal, it is found to be strongly underestimated in Antarctica, while having a good agreement in Europe and most of North America. The computation of anomalies suggest sub-millimeter accuracy in GNSS, assessing its suitability for climate studies, providing that the processing strategy remains unchanged and modifications of hardware or number of GNSS observations were considered in the analysis. The study also concluded that the Southern Hemisphere needs more observations for reanalysis assimilation, suggesting the use of GNSS IWV to accomplish this. However, not only direct comparisons are performed, but also other aspects, like the effect of different spatial resolution in NWP, are studied. [PERSON] et al. [70] evaluated HIRLAM against GNSS network in Northern Europe, along with some forecasts. It was found that relatively coarse resolution (0.4\({}^{\circ}\)) runs had a similar performance to finer resolution (0.21\({}^{\circ}\)) ones. Therefore, a finer resolution did not mean better agreement with observations. It was also found that NWP are useful for detecting erroneous data in GNSS series. Other studies have focused on the causes of discrepancies between NWP and GNSS. The height difference between the orography of the model and the GNSS station can induce systematic differences, since the integrals are not measured from the same base level. This issue was studied by [PERSON] et al.