## 5 Conclusions  
Two sets of experiments have been presented in order to attempt to answer to the scientific question on whether Sentinel satellite constellation data can be used to better understand and predict severe weather events. For this purpose two different events have been chosen. The first event is the Livorno flash-flood producing storm that, according to the criterion in Reference [20] can be classified as type II-short-lived (duration \(<\) 12 h) and very localized (less than 50 \(\times\) 50 km\({}^{2}\)). The second one is the Silvi Marina event that can be classified as type I-long-lived (\(\geq\)12 h) and spatially distributed (more than 50 \(\times\) 50 km\({}^{2}\)). For each event two groups of experiments have been performed, the first one driven by IFS global model and the second one by GFS. For each global model, a set of sensitivity experiments has been executed through the assimilation (with different techniques) of a single observed variable, either from Sentinel or from the GNSS network. Then, building on the results of the single-variable assimilation experiments, few simulations assimilating all the most influencing observations together have been performed. As expected, given their different nature, the two case studies showed different responses to the same type of assimilated observations. In particular, the 3-h cycling 3 DVAR of GNSS ZTD offers good results in both Livorno and Silvi-Marina cases but it is not fundamental in the type-II Livorno event that significantly benefits also from the adjustment of the wind field with high-resolution Sentinel observation. Conversely, the type-I Silvi Marina case takes mostly advantage from a 3-h cycling update of GNSS ZTD and the single time Sentinel observations are more penalized because they allow to adjust the model only the day before the event. Furthermore, IFS and GFS-driven experiments revealed sometimes significantly different responses to the same assimilated variable: this is most probably due to the difference in their respective initial conditions fields. For example, comparing the SM analysis  
Figure 14: IFS-driven cases. QPF values for the 12 h time period from 18 UTC on 9 September 2017 to 06 UTC on 10 September 2017, where a = SST, b = SM, c = WIND, d = ZTD_INSAR, e = ZTD_GNSS3h_1 ist, f = WIND+SM+ZTD_INSAR_GNSS3h_1 ist, g = OBS, h = OL. For details about the 3 DVAR approaches the reader is referred to the main text. fields for the Livorno case, there is a quite prominent difference between GFS and IFS values, namely IFS soil layers look definitely drier than GFS ones. This can lead to different response to the same SM observation. When initialized with GFS initial and boundary conditions, SAR derived SM seems to have an added value with respect to IFS. In general the assimilation of Sentinel and GNSS derived variables always resulted into an improvement in the weather forecast, even if sometimes relatively small for some variables (like SST or LST). This, as discussed in the previous section, could be linked to the partial coverage of the data that are assimilated in the test cases or the already good initial conditions provided by the global models. Thus, it is worth further exploring the synergy between HR Sentinel observations and weather modelling, maybe in different test cases and geographical contexts. Concerning the use of water vapor estimates, the assimilation of both InSAR and GNSS derived ZTD observations has positive impacts on the forecast. These results also pave the way to further explorations of the impact on hydro-meteorological predictive capability of data provided by the potential next-generation Earth Explorer geostationary InSAR satellite [51], currently under evaluation by ESA. This satellite would provide ZTD observations at high resolution both in time and space, combining the advantages of both InSAR and GNSS products, highlighted in this work. Conceptualization, A.P., A.M.G. and L.P.; methodology, M.L., A.M.G., A.P., L.P., N.P. ; validation, M.L., G.B., S.B. ; formal analysis, M.L., A.R.