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.