# 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
RMSE were found to be between 4.4 and 5.7 mm, biases of 0.1-0.4 mm, and correlations of 0.83-0.88. Global Ozone Monitoring Experiment 2 (GOME-2) satellite instrument was later evaluated by [PERSON] et al. [78] in this region, using 21 stations during the years 2007-2012. Bias was found to be 0.7 mm on average, and standard deviation 4.4 mm. They also analyzed some factors that can influence GOME-2 retrieval performance. It was found that increasing solar zenith angle also increases both bias and standard deviation, that cloud presence increases bias but does not have an effect on standard deviation, and that, under cloudless conditions, GOME-2 errors are within its nominal error. Other studies followed in the Iberian Peninsula, like those by [PERSON] et al. [83; 84; 82], who compared Ozone Monitoring Instrument (OMI), MODIS and a set of satellite instruments, respectively, against reference GNSS data. We will focus on the last article, as it includes OMI and MODIS (both NIR and IR products), and also other products like GOME-2, SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY), AIRS, and Spinning Enhanced Visible and Infrared Imager (SEVIRI). They studied the pseudomedian (rather than mean bias) and the inter-quartile range (rather than standard deviation) of relative differences between satellite products and reference GNSS product in nine stations the interior Iberian Peninsula during the period 2007-2013. It was found that all satellite products tended to smooth IWV values (low IWV values tended to be overestimated), while high IWV ones tended to be underestimated. \(R^{2}\) was in the range 0.56 (AIRS) \(-\) 0.83 (GOME-2), pseudomedians between \(-5.2\pm 0.1\%\) (SEVIRI) and \(16.7\pm 0.8\%\) (GOME-2), and inter-quartile ranges between 32.58% (GOME-2) and 47.84% (AIRS). Dependencies on some variables were also studied, namely, solar zenith angle, IWV, season and cloudiness, which highlighted the differences among the different satellite remote sensing approaches. A similar multi-comparison including AIRS, GOME-2, MODIS (NIR and IR), OMI, POLarization and Directionality of the Earth's Reflectances (POLER) and SCIAMCHY against GNSS data in the Ny-Alesund station (European Arctic) during the period 2010-2017 was performed in another work [89]. It was found that NIR had a very low performance in this region, and that some external source for filtering out cloudy scenes is needed. OMI version 4.0 product showed high bias and variability as compared with the other products. The biases were in the range \(-\)2.7-5.9 mm, RMSE 2.4-5.2 mm and \(R^{2}\) between 0.22 and 0.73. OMI IWV product has also been validated in successive versions of its algorithm retrieval [81; 87]. We will only discuss the latest version (4.0) of the algorithm, which was tested against GNSS IWV globally distributed network for land and SSMI/S over the oceans. The influence of cloud scenes is studied, recommending filtering data with cloud fraction less than 0.05-0.25 and cloud top pressure more than 750 mm, apart from quality flag. In the comparison, cloud fraction limit is set to 0.05, with bias of 0.32 mm, standard deviation of 5.2 mm and correlation 0.87 with respect to GNSS. They also demonstrate the usefulness of OMI data for assimilation with WRF. However, on a more local scale, [PERSON] et al. [89] showed that, in the Arctic, OMI-GNSS bias can be very high. Therefore, although OMI product has a good performance globally, it is important to study local performance when using it for specific regions. [PERSON] et al. [80] validated MODIS-NIR IWV against a GNSS station in the Trans-Himalayan region. Bias was \(-0.18\) mm and RMSE 1.37 mm, while correlation was 0.95 for daily data. The comparison was also performed in a monthly/seasonal basis, finding that summer values were worse than in other seasons, which is expected because IWV increases in summer. These values were better than others reported in the literature for this product, probably because of the use of daily data.