# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 3 Inter-Comparisons with Other Techniques
### Validation of Satellite Measurements with GNSS as Reference
Orbiting Carbon Observatory-2 IWV was compared against the SuomiNet global network and other instruments (IGRA's radiosondes and AERONET sun-photometers) as references [79]. RMSE value reported against SuomiNet is 1.3 mm and correlation of 0.994. [PERSON] et al. [85] analyzed the Fengyun-3A Medium resolution spectral imager (FY-3A/MERSI) IWV product against GNSS and sun-photometer measurements in the Tibetan Plateau in 2009-2013. RMSE was in the range 1.87-2.75 mm and bias \(-1.14\)-0.64 mm. [PERSON] and [PERSON] [86] compared FY-3A/MERSI, Terra/MODIS and Envisat/MERIS (all NIR satellite instruments) against GNSS in US. It was found that NIR instruments tend to underestimate IWV under cloudy conditions, as NIR cannot penetrate clouds. RMSE was of 8.644 mm, 5.480 mm and 3.708 mm. RMSE was found to increase under moist conditions. Under clear-sky conditions, \(R^{2}\) was of 0.93 and 0.95 for MERIS and MODIS, and 0.80 for MERSI. [PERSON] et al. [88] validated the satellite instruments, Infrared Atmospheric Sounding Interferometer (IASI), MIRS, MODIS, and MODIS-FUB, against the GNSS network in Germany. The biases were 1.77, 1.36, 1.11, and \(-0.31\) mm, respectively, while RMSE were in the range 2.11-3.77 mm and correlations 0.88-0.96. Monthly means showed similar differences, and no impact was found on time-averaging or low temporal sampling from satellite instruments. Therefore, sampling of IWV seemed to affect the mean values only marginally, but it could have an effect on frequency distribution. Nevertheless, in more challenging regions, satellite estimates of water vapor can improve greatly when averaged in time, as shown by [PERSON] et al. [90]. In this work, monthly IWV products from GOME-2, MODIS and SCIAMACHY were compared against GNSS in three Arctic stations (in the period 2001-2014). The Arctic is known to be a challenging region for satellite retrieval, as high solar zenith angle, high frequency of cloudy skies and presence of snow are problematic conditions for retrieval and remote sensing algorithms. Generally, seasonal biases were less than 1 mm for AIRS, 2.5 mm for SCIAMACHY, and 3.5 mm for MODIS. Moreover, the increase in cloud fraction was related to increasing dry bias. Therefore, authors suggested that more robust information about clouds must be included in the retrievals to reduce the aforementioned biases. The wide variety of satellite products using different instruments and algorithms, together with different quality criteria and the use of different regions for testing, makes it very difficult to extract common conclusions to different works. Even the most used statistical parameters can largely change with different methodological approaches, especially different quality controls. For that reason, we conclude that the scientific community should establish a series of common practices to perform validations in order to make different works comparable. Moreover, it can be concluded that satellite measurements can have very good quality if the right conditions are met. There is a general agreement that cloudy and/or high moisture situations tend to cause worse values of accuracy and precision metrics (i.e., MBE or SD).