## 5 Discussion
This is also reflected in the difference in performance of both MODIS IWV products, which use the same measurements, but different retrieval algorithms. However, sampling affects the shape of IWV distributions, e.g., observed with the bi-modal IWV distribution for MODIS and loss of high IWV values, while for IASI and MIRS, the low spatial resolution leads to even stronger smoothing of IWV values. A closer look at several statistics on IWV differences, corresponding to the analyzed distributions, such as the 10 th, 50 th and 90 th percentile as well as mean seasonal differences, reveals that differences at low and high IWV values as well as a mean seasonal IWV difference can become significantly larger than the overall IWV bias, though the results differ notably per sampling method and satellite retrieval algorithm. While this study helps us to further understand the performances and limitations of the various satellite IWV products over a region of structured terrain in Central-Europe, the results are not necessarily applicable to other regions in the world, especially if dominated by other types of weather conditions. In very cloudy regions, a MODIS IWV climatology could possess a significant bias, while it could become problematic for IASI and MIRS IWV retrievals in very wet or dry regions with extreme low or high IWV values. In a next step, the study can be extended globally making use of the increasing number of worldwide GNSS stations, which covers various climate regions, relevant for climate studies. It would naturally also benefit from including IWV products from other types of instruments/measurements as well. Furthermore, many IWV retrievals and corresponding datasets are under constant development, thus making it preferable to repeat these kinds of analyses on a regular basis and along the process including newly developed IWV datasets. Of particular interest for the impact assessment of spatial sampling on retrieved IWVs could be the inclusion of the newly developed high spatial resolution IWV product (\(\approx\)300 m) from the Ocean and Land Color Instrument (OLCI) on the polar-orbiting satellite Sentinel-3 [40; 41]. **Author Contributions:** Conceptualization, S.S. and S.C.; methodology, C.C.H., S.S., S.C.; software, C.C.H., L.D. and S.S; validation, C.C.H., L.D. and S.C.; formal analysis, C.C.H, L.D. and S.C.; investigation, C.C.H., L.D., S.S., S.C.; resources, C.C.H. and S.C.; data curation, C.C.H., S.S., H.D. and T.A. ; writing-original draft preparation, C.C.H., L.D. and S.C.; writing-review and editing, C.C.H. and S.C.; visualization, C.C.H. and L.D. ; supervision, S.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Bundesministerium fur Bildung und Forschung (BMBF) within the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)\({}^{2}\)) project with grant numbers (Forderkennzeichen) 01 LK1502J and 01 LK1210C. The publication of this article was funded by Freie Universitat Berlin. **Acknowledgments:** We would like to thank [PERSON], who was involved in the first conception of the study, and [PERSON] from Deutsches GeoForschungsZentrum Potsdam for the support concerning GPS IWV data. **Conflicts of Interest:** The authors declare no conflict of interest.