# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 5 Impact of Water Vapor Variability on Meteorology and Climate  
### Assimilation  
NWP models need to ingest real data in the process known as assimilation to be able to perform predictions. Therefore, GNSS data can be an interesting source for NWP assimilation, especially in regions where other sources of atmospheric water vapor information are missing. [PERSON] and [PERSON] [121] showed that GNSS data improved forecasts from NWP models. Particularly they used the Rapid Update Cycle in the US, and assimilated data from 18 and 55 stations. They found improvements in relative humidity (RH) of up to 4.5% for the 55-station experiment and up to 1.0% for the 18-station experiment. Despite being a modest improvement, it must be taken into account that the US had already good knowledge of the moisture from other kinds of measurements already assimilated by the model. Therefore, in other regions this could be enhanced, and even in the US, the improvement was evident, especially in the 3-h forecast. They also developed techniques to retrieve GNSS IWV measurements every 30 min to be available for forecasters and modelers, improving IGS service in latency. Other works from the 2000s found moderated improvements in models as well: [PERSON] et al. [122], who assimilated GNSS IWV from COST Action 716 into the MeteoSwiss NWP model; [PERSON] et al. [123], who assimilated IWV data from 11 GNSS stations in China into the MM5 model; [PERSON] et al. [124], who presented a near real-time GNSS IWV product in the context of the project GASP and assimilated it into the local model from German Weather Service; and [PERSON] et al. [125], who tested the GNSS assimilation in a severe weather event in Beijing. All these works considered the assimilation of the IWV GNSS data. However, it is possible to use more direct data, such as SWD. [PERSON] et al. [126] compared the assimilation of SWD and IWV from the GNSS network and found SWD to be more appropriate for correctly retrieving temperature and moisture profiles. They used a hypothetical GNSS network of 64 stations in the US, using the MM5 4-dimensional variational data assimilation system. [PERSON] and [PERSON] [127] used a similar approach, but assimilated slant water vapor (SWV) into the 3D-variational (3 DVAR). The tests included hypothetical GNSS stations as well and sensitivity analysis, concluding that GNSS SWV assimilation is positive even with the inclusion of typical errors in measurements. The introduction of multi-GNSS processing can improve near real-time measurements in several ways. First, it can provide more accurate data by increasing the number of frequencies used, as new GNSS satellites introduce a third frequency (better combinations of signals). Second, having more satellites available makes possible to increase temporal resolution (below 30 min) and spatial resolution through networks and tomography techniques. [PERSON] et al. [128] performed an experiment in Austria with the use of multi-GNSS (GNSS and GLONASS) network, finding the assimilation of multi-GNSS data very positive and concluding that Galileo constellation inclusion could improve analysis even more. In addition to this, it must be noted that regions lacking dense networks of ground-based GNSS stations can also benefit from GNSS data assimilation. [PERSON] et al. [129] showed that assimilating GNSS data from a single station was enough to locally improve (\(\sim\)10%) the short-term forecasts in an experiment over southern India during the monsoon. Of course, it will be better if there is moisture information from more GNSS stations, but regions lacking moisture information from other sources can benefit from GNSS data even with a single station in the region of interest. If two-frequency stations are not available, it may be possible to use single-frequency receivers for assimilation. This issue is still under study and more research could be useful, as the use of single-frequency receivers can provide very dense networks as compared to dual-frequency ones.