# 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
These works show that GNSS data are high quality but, in any case, the presence of redundant measurements help assess the quality of products. Radiosonde dry bias was also assessed by [PERSON] et al. [63]. This work inter-compared GNSS, radiosonde, Special Sensor Microwave/Imager (SSM/I) satellite instrument (in coastal stations only) and NWP (ERA-40 and National Centers for Environmental Prediction 2, NCEP2) for stations in Africa. The standard deviations were of 3.0, 2.1, 3.6, and 5.2 mm, respectively, while the biases (GNSS - instrument) were 3.2, \(-\)0.6, 0.5, and 1.6 mm, respectively. The results confirmed the high potential of GNSS for water vapor monitoring. Another work found a cause for dry bias in tropical regions [64]: the Integrated Global Radiosonde Archive (IGRA) product only integrates water vapor up to a pressure level of 500 hPa. While this is usually a fair approximation, in tropical regions it induces a dry bias in the radiosonde product; in the case of Tahiti, of 9%. Another interesting region for validation is Antarctica. It is a region with relatively scarce water vapor observation data; yet its water vapor spatial distribution and temporal variability influences precipitation patterns and, therefore, ice accumulation. Hence, a GNSS-derived data set for Antarctica, with proven accuracy and stability, could be very useful for modeling and obtaining trends [58]. A work used homogeneously reprocessed GNSS data for the year 2004 in 12 stations [58] in this region. These data had three important improvements compared to previous GNSS IWV products: first, it used absolute antenna phase center variations. Second, it used VMF1 mapping functions. And third, an accurate a priori model for ZHD. For comparison, radiosonde measurements were used as reference, finding a GNSS bias of \(-\)0.48 mm, very adequate even for a region with low IWV that could affect GNSS sensitivity to water vapor. Atmospheric Infrared sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) IWV products were also compared to GNSS in this work, with low bias (0.58 and 0.35 mm, respectively) and RMSE (1.24 mm and 1.42 mm). This concluded that the newly analyzed GNSS IWV product had enhanced capabilities for assimilation in NWP. Multi-comparisons can help attribute errors to one or another instrument, making easier to analyze deeper the possible causes of error. As an example, a work that compared radiosonde, GNSS, Fourier-transform infrared spectroscopy (FTIR), MWR, Advanced Microwave Sounding Unit (AMSU-B) satellite based microwave radiometer, and ERA-Interim NWP in the location of Kiruna (Sweden) [65] found systematic differences in the range of \(\pm\)1 mm, consistent with other mentioned works. Although the overall comparison shows a good agreement among the different techniques, they aimed to characterize the slope and offset of the different techniques, combining their study with others, but no consistent pattern emerged (the only pattern was that systematic differences were in the 1-2 mm range). This work also presented a method to compare values of IWV with different heights (that is, where the lower limit of the integral starts a different heights), based on a correction factor linearly dependent with height. The factor was obtained with radiosonde data. The representativeness error was also addressed, presenting a method to estimate it and, finding that it can dominate the differences among techniques, what could make it so that observed differences do not contain much information about the true precision of the techniques. This must be taken into account in future works to make validations more statistically significant. Another interesting inter-comparison [66] involved radiosondes, GNSS, sun-photometers, a combination of GOME, GOME-2 and SCIAMACHY products, and AIRS IWV product. The study was carried out in 28 sites in Northern Hemisphere. It was found that biases were in the \(-\)0.3 to \(+\)0.5 mm interval, with larger standard deviation (SD) values (0.96-3.96 mm) specially for satellite instruments.