## 5 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**AIWV** & \(\overline{AI}\) & **10\%** & **50\%** & **90\%** & \(\overline{ITA}\) & \(\overline{SON}\) & \(\overline{DIF}\) & \(\overline{MAM}\) \\ \hline Certain clear sky & 0.24 & 0.10 & 0.70 & \(-\)0.50 & \(-\)1.83 (33.7\%) & \(-\)1.24 (33.5\%) & \(-\)0.89 (13.7\%) & \(-\)1.19 (19.1\%) \\ Daytime & 0.12 & \(-\)0.10 & 0.20 & 0.20 & 0.15 (25.2\%) & 0.05 (25.0\%) & 0.09 (24.1\%) & \(-\)0.14 (25.7\%) \\ Daytime \& certain clear sky & \(-\)0.01 & \(-\)0.80 & 0.50 & \(-\)0.20 & \(-\)1.20 (31.0\%) & \(-\)1.31 (35.5\%) & 0.25 (11.5\%) & \(-\)3.06 (22.0\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of difference between average GPS IWV obtained from various sampling methods and average GPS IWV obtained from all GPS time steps and all GPS stations (\(\Delta\)IWV in kg/m\({}^{2}\)). Results are shown for the average computed from all relevant IWV values within the periods June-August (\(\overline{JIA}\)), September-November (\(\overline{SON}\)), December-February (\(\overline{DJF}\)) and March-May (\(\overline{MAM}\)). Numbers within brackets indicate the relative frequency of IWV values with respect to the complete time period of one year. Please note that the relative frequency of IWV values from all GPS time steps and all stations with respect to the complete time period of one year is about 25% for each season. region. This might be attributed to a low impact of height variations within a satellite pixel as well as of height differences between satellite pixel and GPS station. From the pixel-based IWV products, aggregated statistics were computed in the form of monthly means and compared to the GPS. In general, the annual cycle and corresponding standard deviations are well represented for all instruments. Overall biases obtained earlier are also reflected in the monthly means, thus a best match with GPS was found for MODIS-FUB. Different averaging time periods, going from 1 day to 3 months, hardly influence the difference between mean satellite-based IWV and mean IWV obtained from all 15 min GPS time steps within an averaging time period. This is related to the fact that in Germany the IWV fields and their temporal variability are dominated by large synoptic disturbances occurring on the time scale of about one day. With the use of the GPS IWV product in combination with a SEVIRI cloud coverage product, both on 15 min temporal resolution, the impact of retrieval-specific limitations on IWV distributions and corresponding statistics is assessed. The distributions change into a bi-modal distribution when only daytime and certain clear-sky cases are considered, which fits well with the MODIS bi-modal distributions observed earlier. Also, a loss of high IWV values is observed. From the GPS IWV product, a mean diurnal cycle and satellite-based daily means are computed to assess the impact of the low temporal sampling of instruments on polar-orbiting satellites on mean IWV. The satellite temporal sampling hardly leads to any biases, since the overpass times in the late morning/early afternoon are located around or close to the daily mean for MODIS or, in combination with the night time overpasses, appear to cancel each other out for IASI and MIRS. In addition, the amplitude of the mean diurnal cycle is small, about 0.5 kg/m\({}^{2}\) for the summer time (highest amplitude), especially when compared to the corresponding standard deviations of about 8 kg/m\({}^{2}\), related to the dominance of the noisy large-scale weather systems in Germany. However, when weather anomalies occur larger differences can be expected as fair-weather conditions introduce much stronger daily amplitudes [37]. Combining the validation results with the results of the impact of sampling issues on IWV, temporal and retrieval-specific, show that sampling effects affect mean IWV values only marginally. The overall IWV differences/biases are dominated by the performance of the retrieval algorithms.