Incorporating Sentinel-derived products into numerical weather models: the ESA STEAM project

Abstract

The STEAM (SaTellite Earth observation for Atmospheric Modelling) project, funded by the European Space Agency, aims at investigating new areas of synergy between high-resolution numerical weather prediction (NWP) models and data from spaceborne remote sensing sensors. An example of synergy is the incorporation of high-resolution remote sensing data products in NWP models. The rationale is that NWP models are presently able to produce forecasts with a spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies could be reduced by ingesting high resolution Earth Observation derived products into models operated at cloud resolving grid spacing. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) used NWP models runs. This paper presents the first results of the experiments carried out in the framework of the STEAM project, regarding the ingestion/assimilation of surface information derived from Sentinel data into a NWP model. The experiments concern a flood event occurred in Tuscany (Central Italy) in September 2017. Moreover, in view of the assimilation of water vapor maps obtained by applying the SAR Interferometry technique to Sentinel-1 data, the results of the assimilation of Zenith total delay data derived from global navigation satellite system (GNSS) are also presented.