INGESTION OF SENTINEL-DERIVED REMOTE SENSING PRODUCTS IN NUMERICAL WEATHER PREDICTION MODELS: FIRST RESULTS OF THE ESA STEAM PROJECT

The European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims at investigating new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites. An example of synergy is the ingestion of surface information derived from Sentinel data in numerical weather prediction models. The rationale is that Sentinels 1, 2 and 3 are able to provide high spatio-temporal resolution information on the surface boundary (as well as the atmosphere column) and that an inaccurate representation of the boundary conditions represents a major source of uncertainty for weather forecasts. For a profitable ingestion of EO data in numerical weather prediction models, a critical aspect is the choice of a suitable model. Once the numerical model is chosen, the problem of the selection of the Sentinel-derived surface variables that have to be ingested in the model has to be tackled. While some data, such as sea and land surface temperature, are directly available, other surface data, such as soil moisture, have to be retrieved. Being STEAM currently in its initial phase, this paper gives a general overview of the project and focuses on the first activities performed in its framework. In particular, it describes the rationale behind the choice of the Numerical Weather Prediction Model and the multi-temporal approach designed to retrieve soil moisture from Sentinel-1 data. Moreover, the first results of the ingestion of Sentinel derived soil moisture, land surface temperature and sea surface temperature data into the selected model are shown. These results concern an extreme weather event that occurred in Tuscany (central Italy) in September 2017.