## 4 Validation and Results
### Silvi Marina Test Case  
Figures 13 and 14 summarize the QPF values of the different experiments for the 24 h time period from 00 UTC on 15 November 2017 to 00 UTC on 16 November 2017. Figure 13 refers to the GFS-driven  
Figure 11: Comparison between the OL simulated structure with respect to the 3 DVAR-WIND simulated structure at 02 UTC of 10 September 2017. Panels (**a**,**c**) report the 3D simulated structure composed by rainwater (cyano) graupel (yellow) and snow (grey) microphysical species respectively for OL (**a**) and 3 DVAR-WIND (**c**) simulations with the horizontal 10m wind intensity represented by red vectors. The red line in Panels (**a**,**c**) indicates the location of the vertical section of the reflectivity values in the middle of the convective structure shown in panels (**b**) for OL and (**d**) for 3 DVAR-WIND. Figure 12: Comparison between the IFS and GFS OL 10 m wind field with respect to the IFS and GFS 3 DVAR-WIND 10 m wind field during the entire event duration: 01, 02 and 03 UTC of 10 September 2017. Panels (**a**,**e**,**i**) refer to IFS OL, panels (**b**,**f**,**j**) refer to IFS 3 DVAR-WIND, panels (**c**,**g**,**k**) refer to GFS OL and panels (**d**,**h**,**l**) refer to GFS 3 DVAR-WIND. The red vectors represent wind direction while in colours the wind field intensity is represented. cases, while Figure 14 refers to the IFS-driven ones. The MODE analysis results for the three different thresholds are reported in Table A7 (24 mm), Table A8 (48 mm) and Table A9 (72 mm). The best performances are achieved with the ZTD assimilation from GNSS observations (ZTD_GNSS3h_1 ist), both for GFS and IFS-driven cases (Tables A10 and A11). It is important to highlight that, since this is a type-I event, the large scales are more important in controlling the dynamics with respect to the Livorno case. This is the reason why the OL runs perform quite well, as discussed in what follows. More specifically, comparing the MODE spatial indices with respect to the OL ones, one can say that, in general, the assimilation of the wind field and/or the columnar water vapor content improve the forecast at all levels of rain intensity. This is interpreted with the fact that all the Sentinel derived fields concur to provide a better localization of the most intense core of the observed event, thanks to a better representation of the spatial distribution of the water vapor and/or the structure of the moisture flux. As in case of the Livorno experiment, additional important comments can be drafted, when analyzing overall the rain depth threshold values. The distance of the simulated center of mass of the rainfall field with respect to the observed one seems to benefit at most from Sentinel fields when IFS-driven experiments are compared with IFS OL, while the orientation of the rainfall area gets more realistic in general from Sentinel products for lower threshold (24 mm) both in case of GFS and IFS. This statement holds true also for higher threshold in case of GFS-driven experiments, while IFS OL behaves very well for this metric when the most intense core of the rainfall field is targeted, thus making harder to outperform it. Assimilating Sentinel products has in general a positive impacts on the spatial extent both for GFS and IFS-driven experiments, especially at low and intermediate thresholds. Concerning the overlap between the simulated and the observed rainfall areas, at low (24 mm) and intermediate (48 mm) thresholds, both GFS and IFS-driven experiments exhibit an improvement because of ingestion of Sentinel products. The maximum rainfall value benefits at most from Sentinel products at lower threshold (24 mm) both for GFS and IFS experiments. This is not true for 72 mm threshold but it is worth mentioning how in that case both GFS and IFS OL achieves values well above 0.8 and rather close to the maximum, equal to 1.