## 4 Validation and Results
### Livorno Test Case
It is possible to see that the average SM observed by Sentinel over the AOI is closer to the IFS-driven case values with respect to the GFS ones. In fact, Figure 10 reveals bigger differences in the SMGFS-SMS1 picture (on the left) with respect to the SMIFS-SMS1 one (on the right). This means that the assimilation in the GFS experiment produced a larger change with respect to the assimilation in the IFS one, leading to a bigger impact on the forecast. It is also worth noticing that the assimilation of wind, soil moisture and ZTD all together ranks well for both driving Global Circulation Models (GCMs). Thus, for operational purposes, this can be a recommended assimilation setup possibly with a 3-h cycling 3 DVAR because this setup allowed to obtain an improvement both in the timing and the intensity of the rainfall peaks, as highlighted in Reference [12] for the GFS-driven experiment. Figure 8: GFS-driven cases. Quantitative Precipitation Forecast (QPF) values for the 12 h time period from 18 UTC on 9 September 2017 to 06 UTC on 10 September 2017, where (**a**) = LST, (**b**) = SST, (**c**) = SM, (**d**) = WIND, (**e**) = ZTD_GNSS3h_list, (**f**) = ZTD_GNSS3h_list, (**g**) = WIND+SM+ZTD_GNSS3h_list, (**h**) = WIND+SM+ZTD_GNSS3h_list_18 UTC (only 18 UTC), (**i**) = OBS, (**j**) = OL. For details about the different ingestion approaches the reader is referred to the main text. Figure 9: 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**) = LST, (**b**) = SST, (**c**) = SM, (**d**) = WIND, (**e**) = ZTD_GNSS3h, (**f**) = ZTD_GNSS3h_list, (**g**) = WIND+SM+ZTD_GNSS3h_list, (**h**) = WIND+SM+ZTD_GNSS3h_list_18 UTC (only 18 UTC), (**i**) = OBS, (**j**) = OL. For details about the 3 DVAR approaches the reader is referred to the main text. To gain a deeper insight of the modelling experiments, the best performing IFS-driven case, namely the WIND one, is compared with the OL. In particular, Figure 11 shows the atmospheric state of the two experiments at 02 UTC on 10 September 2017, corresponding to the most intense phase of the observed event, displayed with the VAPOR (Visualization and Analysis Platform for Ocean, Atmosphere and Solar Researchers, www.vapor.ucar.edu) software. Over the same scene (Figure 11), 3D isosurfaces (\(5\times 10^{-5}\) kg/kg) for the rainwater, snow and graupel variables have been rendered in combination with the wind field at 10 m in case of the OL (panel a) and IFS 3 DVAR-WIND case (panel c). The results show that over the area interested by the most intense observed rainfall phenomena (blue circle) the OL run is not able to produce any significant convective phenomena, while the IFS 3 DVAR-WIND does so. This is also visible in the vertical cross sections of the reflectivity field for the two modelling experiments (green circles panels b and d): while reflectivity is nearly absent nearby the Tuscany coastlines (Livorno area) for the OL experiment (panel b), conversely strong activity is apparent in panel d over the Livorno area and downshear the main convective system, with values peaking up to 50-55 dBz. Similar improvement is achieved also in the GFS-driven case, as shown in Reference [12]. To better understand the impacts of the wind assimilation, the instantaneous 10 m wind field during the intense phase of the event is shown in Figure 12. It is important to notice that one of the most important ingredients responsible of the triggering of this kind of highly-precipitating back-building Mesoscale Convective System (MCS) is the persistence and intensity of the convergence line over the sea in the area in front of the coast hit by the event [19]. Figure 12 shows that IFS OL is not able to produce a significant convective phenomenon over the area hit by the event, because the convergence line over the sea is shifted to the South with respect to the real location for the entire event duration. Conversely, IFS WIND assimilation is able to produce a stronger convergence line with better localization and persistence.