# 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
[71], who compared Japanese 55-year Reanalysis (JRA-55) and GNSS in 26 Japanese stations in the period July 2010 to December 2012. After JRA-55 was corrected to account for the height difference between the GNSS station and the JRA-55 integral base level of geopotential, some dry bias in JRA-55 IWV still remained. This suggested that not all of the discrepancy in IWV could be attributed to the height difference and, therefore, the hydrological cycle of this reanalysis needed deeper analysis. Moreover, NWP can provide inaccurate data when the observations assimilated have erroneous or biased data. For example, some radiosonde models have a well-known issue that caused a dry bias. To study this problem, [PERSON] and [PERSON] [72] compared radiosondes and NWP (ECMWF Integrated Forecasting System, and NCEP reanalysis I and II) against GNSS IWV in western Africa over 2005-2008. In this case, the Vaisala RS80-A radiosonde had a dry bias leading to errors in NWP through assimilation, causing a dry bias of NWP with respect to GNSS. Other two radiosonde models (Vaisala RS92 and MODEM) seemed to cause a wet bias in NWP models. Another issue is how a pixel from NWP is representative of a point (i.e., a GNSS station) in the area it covers. [PERSON] and [PERSON] [73] addressed the representativeness error that ERA-Interim can have with respect to GNSS in stations distributed globally. A \"representativeness error statistic\" was developed, showing a correlation of 0.73 with standard deviation of IWV differences between ERA-Interim and GNSS. This statistic is based on the information contained in the ERA-Interim data-set for the four surrounding pixels for the GNSS station. To reduce the representativeness error, it was recommended to bilinearly interpolate data to the position of the GNSS station, as well as time-averaging. Some problematic cases (stations whose data do not fit as well as the rest) were discussed. Nevertheless, not only is IWV tested in NWP, but also its trends. Trends of IWV are very important for discussing how climate is evolving over the years. For this reason, [PERSON] [74] discussed whether reanalysis data can provide valid trends for IWV and other variables. Particularly, IWV and its trends, computed with the ERA40 and NOSAT experiment (reanalysis without satellite data assimilation to mimic the pre-satellite era), were compared globally against GNSS data. ERA40 trends were corrected to avoid spurious trends due to changes in the observation system. In any case, the trends could not be properly reproduced when compared to GNSS data. Later, [PERSON] et al. [75] studied trends from ERA-Interim and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) IWV data-sets for the period 1980-2016, as well as for 104 GNSS sites globally from 1995 to 2010. It was found that ERA-Interim was generally consistent with GNSS trends, while MERRA-2 tended globally to more moistening trends. Inconsistent trends between both reanalyses were found in Antarctica and most of the Southern Hemisphere, as well as central and Northern Africa. These regions have a high uncertainty in both reanalysis, due to the low number of in-situ observations, and the dispersion between one model and the other is generally high. Arid regions are found to have an important dependence of water vapor on circulation patterns, and therefore, in those regions the Clausius-Clapeyron scaling ratio is not a good proxy to infer IWV inter-annual variability and decadal trends. GNSS IWV products have proven to be a reliable source of data for testing NWP performance. However, some issues must be addressed, like the homogenization of time-series (specially when dealing with trend estimation) and possible height differences between GNSS station and pixel in the orography of the NWP.