# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 2 GNSS Methodologies
### Combining with Other Measurements  
Being the atmospheric water vapor highly varying in space and time, the combination of different data-sets with different characteristics can enhance the knowledge of its spatial and temporal distribution. For example, [PERSON] et al. [45] proposed an algorithm to use  
Figure 3: Schematics of the function-based tomography [43]. The satellite and receiver images are originaly made by Freepik ([[https://www.freepik.com](https://www.freepik.com)]([https://www.freepik.com](https://www.freepik.com))) and Flaticon ([[https://www.flaticon.com/](https://www.flaticon.com/)]([https://www.flaticon.com/](https://www.flaticon.com/))) (accessed on 10 June 2021).  
GNSS-derived water vapor measurements to improve humidity profiles estimated from middle and upper atmosphere radar with the radio acoustic sounding technique.  
The use of satellite measurements with GNSS data is an interesting idea, since satellite instruments have opposed characteristics to GNSS: high spatial, but low temporal resolution. They are also generally not available for all weather situations, but GNSS data are available for all weather situations without important quality loss. One example of this approach [46] used MEdium Resolution Imaging Spectrometer (MERIS) satellite observations combined with GNSS measurements to estimate IWV by means of a kriging methodology. Thus, IWV was retrieved at a time and point in space by means of (1) a kriging combination of GNSS IWV from several locations at that same time; and (2) MERIS IWV from that point in space at the closest time available. Another example of combination of satellite and GNSS IWV was provided by [PERSON] and [PERSON] [47]. This approach used Meteosat-10 data to identify cloud features, and then using this information in the GNSS IWV interpolation (Delaunay triangulation or kriging interpolation) process. Using this approach in the Israel area, and validating against radiosonde, the obtained bias and RMSE were 1.77 and 2.81 kg m\({}^{-2}\) to 0.74 and 2.04 kg m\({}^{-2}\), respectively.  
Other works combine GNSS data with models. An experiment ingested GNSS and Interferometric synthetic-aperture radar (InSAR) IWV and the Weather Research and Forcasting (WRF) model to cover areas that are underrepresented by GNSS/InSAR stations, using a fixed-rank kriging approach [48]. The results were compared to MERIS imagery, and uncertainties of less than 1 mm were found. One of the strengths of this technique is that more data sources can be used, improving further the quality of the generated IWV maps. Another hybrid data-fusion model has been proposed, using GPT2 model as base, and correcting it with polynomial fitting and spherical harmonics functions obtained from GNSS and radiosonde IWV, also considering correction for height differences associated to each station [49].  
Data fusion approaches allow increasing even more the benefits of the continuously operating GNSS networks, which have the potential to enhance weather forecasts and analysis. When applying them, it is important to correct the IWV for height differences, since IWV decrease with increasing height.