## 1 Introduction  
The advance of numerical weather prediction (NWP) models to increasingly higher grid spacings (km and sub-km) has lead to new grounds being reached for what concerns their potential synergy with spaceborne systems. On the one hand, to feed high-resolution (HR) NWP models, HR input data and boundary conditions potentially updatable on, at least, a weekly basis are needed. On the other hand, the present state of the art HR NWP models coincides with the availability of several Earth Observation (EO) data characterized by high spatial and/or temporal resolution, such as the Sentinel missions developed by the European Space Agency (ESA) in the framework of the Copernicus programme (EU Regulation No 377/2014). Copernicus is a space infrastructure conceived to deliver worldwide and timely EO data. The free data policy of Copernicus and the fact that it is a long term programme (all investments are secured for 20 years of lifetime and the second generation of Sentinel satellites is in preparation) makes the use of Copernicus data very attractive, not only for the Remote Sensing community. At present, services like emergency management, land surface and marine applications make use of Sentinel data ([[https://www.copernicus.eu/en/services](https://www.copernicus.eu/en/services)]([https://www.copernicus.eu/en/services](https://www.copernicus.eu/en/services))), while Copernicus atmospheric services focus on air quality and atmospheric composition, ozone layer and ultra-violet radiation, emissions and surface fluxes, solar radiation and climate forcing but not on NWP applications. Taking advantage of Copernicus Sentinel data even for HR NWP applications is a challenging but very attractive problem, because Sentinel data have a clear potential to provide additional information for this kind of applications. In fact, Sentinel missions are able to provide high spatio-temporal resolution information over the surface boundary. Moreover, the Synthetic Aperture Radar (SAR) Interferometry (InSAR) technique [1; 2] applied to Sentinel-1 SAR data enables to retrieve HR spatial--albeit low-temporal--information on the spatially and temporally highly variable atmospheric water vapour fields [3; 4; 5]. To increase the temporal frequency of EO derived atmospheric water vapour data, Sentinel-1 estimates can be complemented by GNSS (Global Navigation Satellite Systems) data. These are point measurements, thus having a coarser resolution but they reach a temporal resolution of 30 s.  
NWP models' boundary conditions observations, such as soil moisture, land and sea surface temperature, are currently provided at coarse spatial and/or temporal resolutions, strongly sub-optimal for this application. Poor representation of these boundary conditions is thought to be a significant source of weather forecast uncertainties [6]. It can be expected that ingesting products derived from Sentinel data and/or GNSS measurements into NWP models might significantly reduce severe weather forecast uncertainties. While some investigations on the assimilation in NWP models of low resolution (tens of km) EO derived products (e.g., soil moisture extracted from the Soil Moisture and Ocean Salinity mission data), are available in the literature [7], few studies were conducted on the ingestion of HR EO products. Only a few papers on the assimilation of water vapour maps derived from InSAR data [8; 9; 10] and one paper on the ingestion of SAR derived soil moisture maps [11] are available in the literature. These works consider a small spatial domain [8; 10] or grid spacing at the cloud-permitting limit (3 km [9]). Hence, to the best of the authors knowledge, there is currently no investigation that combines a big domain with a very high grid spacing (of the order of 1 km, in the cloud resolving range) and considers more than only one EO derived variable.