## 1 Introduction
Thus, the ingestion of high spatial and/or temporal resolution EO products (in particular Sentinel ones, that have been used only in few works, such as Reference [9]) in HR NWP models appears to be a significant contribution to the scientific literature. The scientific questions at the base of the \"SaTellite Earth observation for Atmospheric Modelling\" (STEAM) project (awarded in 2017 by the European Space Agency (ESA)) concern the synergistic use of high-resolution numerical atmosphere models with space-borne systems. A subset of experimentswere presented in Reference [12] with a focus on operational issues, while in this study the complete outcomes of the STEAM project are presented. This paper introduces and discusses the results of a set of experiments on the assimilation in the Weather Research and Forecasting (WRF) model [13], of surface information derived from Sentinel products and information about the atmospheric water vapour content derived from both Sentinel-1 data and GNSS measurements. The objective is to verify whether and to which extent this ingestion produces benefits in terms of the uncertainty in severe weather forecast. In the framework of the STEAM project, we decided to focus on the Mediterranean region because it is frequently struck by severe floods and flash floods causing impressive losses of lives and several million euros of damages every year [14; 15]. This kind of extreme events are generally caused by intense rains that result from the complex interactions between the humid and warm air flows coming from the sea and the steep orography that characterizes the coastlines of the basin, for example, Reference [16] and references therein. These intense meteorological events are still challenging from a forecasting point of view because they are often controlled by a combination of relatively small-scale processes (such as surface convergence lines, interaction with the orography or the sea surface temperature, low level moist jets, to cite some), which can be very hard to forecast [17; 18; 19]. In addition, the Mediterranean region (in particular the Italian territory) is covered by a dense network of rain gauges, allowing for a reliable evaluation of the quantitative precipitation forecast (QPF) quality. Several simulations are performed to assess the possible benefit of assimilating either a single EO Sentinel derived variable or a combination of them. In the STEAM project framework two test cases are considered, a flash-flood producing storm occurred in the Livorno town (Tuscany, Central Italy) in September 2017 and a precipitation event generating a flood in the Silvi Marina village (Abruzzo, South Italy) in November 2017. The set of experiments includes the open loop (i.e., no ingestion of any variable in the WRF model), used as benchmark and some runs with the ingestion of one variable at a time and the ingestion of a combination of variables. The paper is organized as follows. Section 2 describes the spatio-temporal evolution of the two selected case studies and the WRF model setup. There is a special emphasis on the cloud-resolving grid spacing nature (1.5 km) of the performed simulations, over 1200 \(\times\) 1200 km\({}^{2}\) wide area to accommodate large portions of ESA Sentinel snapshots. Section 3 describes the data assimilated and their respective data assimilation procedure. Section 4 presents validation of model outputs and the results discussion. Finally, in Section 5 some conclusions and future works are highlighted.