## 4 Results
### Impact of Temporal and Satellite Retrieval-Specific Sampling on IWV Aggregations and Distributions
Here, a small diurnal cycle is observed and the MODIS temporal sampling between 10 h and 15 h LT coincides with IWV values that are on average slightly higher than the daily mean. A slightly less pronounced effect can be observed in the summertime, for which also IASI shows a small positive bias of about 0.15 kg/m\({}^{2}\). In general, the satellite overpass times in the late morning and early afternoon (10 h-15 h LT) coincide well with the phase of the IWV mean diurnal cycle that is close to the IWV daily mean. Similar findings for MODIS, but on a global scale, are shown in [PERSON] et al. [17]. For IASI and MIRS, the increased temporal sampling at additional daily periods during the evening and nighttime appear to lead to a cancelling out of various deviations from the daily mean, resulting in absolute biases below 0.1 kg/m\({}^{2}\). Figure 9 shows the impact of temporal sampling and retrieval-specific characteristics/limitations for each satellite instrument, now also making use of the actual satellite IWV values. Compared are distributions and corresponding statistics obtained from (1) IWV values from all GPS timesteps, (2) GPS IWV values for timesteps matched with satellite observations, (3) GPS  
Figure 7: IWV distributions for all GPS IWV observations, for all GPS IWV observations during daytime as well as for certain clear-sky and certain cloudy cases. matched with satellite observations with a valid IWV retrieval, (4) all valid satellite IWV values. Going from step 1 to step 2, i.e., the impact of temporal sampling, leads to a slightly lower frequency of lower IWV value and slightly higher frequency of mid-to-high IWV values, thus resulting in a higher mean IWV value. Going to step 3, i.e., including the impact of retrieval limitations such as daytime or clear-sky cases, results in a higher frequency of lower IWV values as well as in a lower frequency of higher IWV values. For MODIS and IASI this could be related to the filtering out of cloudy cases, which are related to higher IWV values, see Figure 7. Now both MODIS retrievals also show a much clearer bi-modal distribution with a minimum around 9-10 kg/m\({}^{2}\), which corresponds well with the result presented in Figure 7 for the IWV distribution under daytime and clear-sky conditions. Going to step 4, i.e., considering the performance of the satellite retrieval algorithms, the difference to step 1 becomes largest for all instruments showing that the overall retrieval bias has the largest impact on the distributions and its corresponding statistics. IASI shows a much lower number of higher IWV values, while MIRS misses many lower IWV values. This'smoothing' is related to the large satellite pixel sizes of the instruments and is also reflected in the lower standard deviations. MODIS-FUB still shows a clear bi-modal distribution, while for MODIS the second bump is smoothed out and shows a much larger number of higher IWV values resulting in a clear wet bias. Figure 8: Hourly deviations from the IWV daily mean (zero black dashed line) computed using all 15 min GPS IWV values from the one-year time period (black line) and using all 15 min GPS IWV values from the years 2007–2013 (grey line). Overlaid are the deviations of the satellite daily means, computed using the GPS IWV values for matched GPS-satellite cases, to the GPS daily mean from the one-year time period (colored horizontal lines). The relative frequency of occurrence of satellite IWV values at corresponding hourly time intervals is shown in the lower parts of the panels. Results are presented for local time (LT) for four seasons: June–July–August (JJA), September–October–November (SON), December–January–February (DJF), March–April–May (MAM). We have seen that while the shape of IWV distributions can change significantly due to changes in sampling method, the mean values only show minor changes. The limitations of daytime and clear-sky cases do not seem to impact mean results significantly.