## 3 Observational Data Description, Assimilation and Experimental Design
### Experimental Design  
To comply with the objective of the STEAM project (discussed in the Introduction), for extreme events investigation, it was crucial to choose test cases with Sentinel 1 and 3 observations in a reasonable, considering the satellite revisiting times (6 days for Sentinel 1 (every 6 days over Italy there are two passages, one in the morning and another in the evening) and 1-2 days for Sentinel 3), temporal interval before the event. The available observations that follow this requirement are introduced in Sections 3.3 and 3.4. Furthermore, to assess the impact on the forecast of each satellites product it was essential to perform a set of experiment assimilating one variable at a time and afterwards one or more experiments assimilating a combination of the most influencing variables. This produced a set of 7 to 9 experiments (for Silvi Marina and Livorno test case respectively). Table 3 summarizesthe experiments methodology, data assimilated (alone or their combinations) and timing for the two use cases. To further consider also the assimilation response to different initialization each set of experiments is performed with two different Global Circulation Models (GCM) namely IFS and GFS. Consequently, for each use case 14 to 18 (for Silvi Marina and Livorno use case respectively) numerical weather modeling experiments were investigated for a total of 32. Table 3 third column summarize the assimilation technique used for each experiment. The main reason of using different assimilation technique deal with the STEAM project aim. For each EO derived variable the most suitable way of assimilation is chosen to assess the impact of Sentinel satellites products assimilation. It was necessary above all to take into account their revisiting time. Furthermore, another important aspect to deal with is the observation type available from Sentinel, how it is modeled and evolved by the WRF model and what was already available in the WRFDA suite for each type of observation assimilation. Thus, concerning wind field and Zenith Total Delay, the 3 dvar technique was chosen among the available assimilation technique because it allows both an assimilation to update initial condition when the observation is available only at a given time (i.e., SM for Livorno use case) and a cycling assimilation technique when observations are available with higher temporal resolution (i.e., ZTD from GNSS). Concerning sea surface temperature and land surface temperature, they are boundary observations, so they were ingested through a direct insertion technique. Finally, the soil moisture is again a boundary condition evolved in time by the model with a 1D (vertical) soil model discretized in 6 vertical levels. Thus, it was assimilated with a nudging-like (it doesn't take into account model and observation weights, refer to Section 2.3 for the details). As it is possible to see from Table 3 the two use cases have slightly different experiments. One of the reason is linked to the absence of LST observation for the Silvi Marina use case and of InSAR ZTD for the Livorno one. While, another different is in the ZTD from GNSS assimilation: for the Silvi Marina use cases only one of the two metodologies applied in the Livorno case is implemented because it was the best experiment setup the Livorno use case and it is also the most compliant one with the R matrix assumption of uncorrelated observation (see Section 2.3). The Livorno case study is initialized at 18 UTC on 8 September 2017, while the Silvi Marina one is initialized at 00 UTC on 14 November 2017. Both use cases have a 3 h update of the boundary conditions and a 48 h forecasting time interval.