# 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  
Table 3 presents a summary of the main findings of the works used to develop this subsection. GNSS IWV data have been widely used as a reference to validate satellite IWV products. Satellite radiometers use different regions of the electromagnetic spectrum in which water vapor have absorption bands. One of the most common is the infra-red (IR) or near infra-red (NIR), in which water vapor is the main absorbent when compared to the rest of atmospheric species. However, liquid water masses, like clouds, can disrupt the signal. This is the so-called screening effect: water vapor below the cloud remains \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
[70] & 1999 & 25 & HIRLAM & 1995-08 & 1995-11 &
\begin{tabular}{c} Northern Europe \\ (Finland, Sweden) \\ \end{tabular} & \(-\)0.1 & 2.3 & 0.94 \\ \hline
[67] & 2000 & 5 & \
\begin{tabular}{c} HIRLAM, \\ radiosonde \\ \end{tabular} & 1996-12 & 1996-12 &
\begin{tabular}{c} Madrid Sierra \\ (Spain) \\ \end{tabular} & \(-\)0.4 & 2.0 & - \\ \hline
[68] & 2001 & 25 &
\begin{tabular}{c} BALTEX; European, \\ SSM/I, WVR \\ \end{tabular} & 1995-08 & 1995-10 & Finland, Sweden & 2.5–3.0 & 2.8 & 0.92 \\ \hline
[74] & 2004 & 160 & ERA-40; NOSAT & 2000-07 & 2001-01 & global & \(-\)31.26–10.75 \% & - & - \\ \hline
[60] & 2005 & 20 & \
\begin{tabular}{c} MWR, Alpine Model, \\ radiosonde \\ \end{tabular} & 2001-01 & 2003-06 & Switzerland &
\begin{tabular}{c} \(-\)2.8 (forecast), \\ \(-\)1.5 (analysis) \\ \end{tabular} & - & - \\ \hline
[72] & 2009 & 11 &
\begin{tabular}{c} ECMWF, NCEP, \\ radiosonde \\ \end{tabular} & 2005-01 & 2008-12 & Africa & - & - & - \\ \hline
[75] & 2018 & 104 & ERA-Interim & 1995-01 & 2010-12 & global & - & - & - \\ \hline
[73] & 2019 & 120 &
\begin{tabular}{c} 2010-12 \\ 2010-12 \\ \end{tabular} & global & \(-\)1–1 & \textless{}2 & - \\ \hline
[71] & 2020 & 26 & JRA-55 & 2010-07 & 2012-12 & Japan & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies on validation of NWP data. 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). \"hidden\" to the satellite radiometer, resulting in a dry bias. This is not always reflected in the comparisons, probably due to corrections in the retrieval algorithms. Another common region for water vapor sensing is the visible, typically red or blue bands. In this case, we have the same problem as with infra-red: clouds are opaque to visible light. In addition, water vapor bands are not so strong in this region, and therefore the sensitivity will not be as high as in the IR case. However, this can be an advantage in some situations. When there is a high amount of IWV, the infra-red absorption can saturate and therefore we would not be able to sense water vapor over a certain threshold. Nevertheless, in that case, the visible bands would not saturate. The microwave, which GNSS also use, is another common region for extracting information about water vapor. This region has the advantage of being more independent of clouds. However, passive systems (that is to say, radiometers that just receive the electromagnetic radiation reflected by the Earth's surface and atmosphere) receive very small amounts of microwave energy. This makes it difficult to properly sense water vapor with this kind of instrument. As we show in the following lines, different works provide very different values depending mostly on three parameters: the region studied, the specific satellite product and the quality control applied. Other sources of differences can be the use of averaged data (daily or monthly means, for example) and the period involved.