# 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
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Work** & **Year** & **N** & **Instr.** & **Start** & **End** & **Region** & **Bias** & **DEV** & **Corr** \\ \hline
[76] & 2008 & 375 & AIRS & 2004-04 & 2004-10 & US & 0.5–1.2 & 3.0–4.5 & 0.91–0.98 \\ \hline
[77] & 2013 & 18 & MODIS & 2002-01 & 2008-12 & Iberian P. (Spain) & 0.1–0.4 & 4.4–5.7 & 0.83–0.88 \\ \hline
[66] & 2014 & 28 & radiosonde, AIRS, & & & & & \\  & & & GOME, GOME-2, & 1995-01 & 2011-04 & Northern Hemisphere & - & - & - \\  & & & SCIAMACHY, & & & & & & \\ \hline
[78] & 2015 & 21 & GOME2 & 2007-01 & 2012-12 & Iberian P. (Spain) & 0.7 & 4.4 & 0.84 \\ \hline
[79] & - & MWR, radiosonde, & 2014-09 & 2016-02 & global & \textless{}0.5 & 1.3 & 0.994 \\ \hline
[80] & 2016 & 1 & MODIS-NIR & 2005-01 & 2012-12 & Trans-Himalayan & \textless{}0.18 & 1.37 & 0.9848858 \\ \hline
[81] & 250 &
\begin{tabular}{c} OMI, SSMIS, \\ sunphotometer \\ \end{tabular} & 2005-01 & 2009-12 & global & - & - & - \\ \hline
[82] & 2017 & 9 & OMI & & & & & \\ \hline
[83] & 2017 & 21 & MODIS & 2007-01 & Iberian P. (Spain) & 0.9\% &
\begin{tabular}{c} 39.36\% \\ (IQR) \\ \end{tabular} & 0.78 \\ \hline
[84] & 2018 & 9 &
\begin{tabular}{c} AIRS, GOME-2, \\ MODIS, OMI, \\ SCLAMACHY, SEVIRI \\ \end{tabular} & 2012-12 & 2012-12 & 2012-12 & 2012-12 & 2012-16.7\% & - & 0.75–0.91 \\ \hline
[85] & 1 & FengYun-3A-MESI, & & & & & \\  & & MODIS, sunphotometer & 2013-07 & Tibetan Plateau & \textless{}1.14–0.64 & 1.87–2.75 & - \\ \hline
[86] & 2019 & 370 &
\begin{tabular}{c} ENVISAT-MERIS, \\ FengYun-3A-MESI, \\ Terra-MODIS \\ \end{tabular} & 2010-01 & 2010-12 & US & \textless{}5.9–5.6 & 2.5–12.4 & 0.73–0.94 \\ \hline
[87] & 303 & OMI, SSMIS & 2006-01 & 2006-12 & global & \textless{}3–4 & 3–10 & - \\ \hline
[88] & 300 &
\begin{tabular}{c} IASI, MIRS, MODIS, \\ MODIS-FUB \\ \end{tabular} & 2012-05 & 2014-06 & Europe (Germany) & \textless{}1.7–1.4 & 2.52–3.77 & 0.88–0.96 \\ \hline
[89] & 2020 & 1 & AIRS, GOME2, MODIS, & 2010-01 & 2017-12 & European Arctic & \textless{}2.67–5.87 & 2.36–7.32 & 0.47–0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of studies on validation of satellite measurements. Column Year is the publication year. N is the number of stations. Instr. is the instruments (besides ground-based GNSS) used in the study. Start and End mark the period considered in the study, in year-month format. Bias is the bias in mm, and DEV is the RMSE or SD also in mm. Corr is the correlation coefficient (R). One of the first validations of satellite IWV products using GNSS as a reference was carried out by [PERSON] et al. [76]. They compared AIRS IWV retrieval against GNSS data from a network in the United States (375 stations) during April-October 2004. Bias (AIRS-GNSS) were in the range 0.5-1.2 mm, while RMSE values were between 3.0 and 4.5 mm. Correlations (0.91-0.98) were calculated monthly. The biases were found to have a seasonal behavior (from negative in April, to peak positive in July and then decreasing). It was concluded that GNSS data are an excellent tool for satellite validation, because they allow all-weather verification, with a high number of pairs thanks to their high temporal resolution (30 min in this work), and their accuracy. The quality of AIRS retrievals was assessed. The Iberian Peninsula has been widely tested, in part because of two main reasons: first, it is a region that gives home to different climate regimes in a relatively small area and, second, it is a region with a relatively dense network of GNSS stations and collocated (or nearby) meteorological stations. [PERSON] et al. [77] studied the performance of MODIS-IR and NIR IWV annual cycle against ground-based techniques, mainly GNSS, in 18 locations in the Iberian Peninsula. The comparison is very detailed by zones of this region and by season, finding high differences in MODIS performance between coastal and interior stations. Representativeness error is also studied, finding that using only time-coincident data for monthly means improves the agreement between MODIS and GNSS.