METAWAVE / STEAM

The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied-a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.

In many cases, the largest uncertainty in synthetic aperture radar interferometry (InSAR) is a range delay caused by the presence of atmospheric water vapour resulting in a phase disturbance. This limits the accuracy of InSAR products such as digital elevation models (DEMs) and terrain subsidence maps. The quality of these products could be dramatically improved if atmospheric water vapour effects could be corrected for. The ESA METAWAVE (mitigation of electromagnetic transmission errors induced by atmospheric water vapour effects) project primarily aims at improving the accuracy of InSAR products by correcting for atmospheric effects in novel ways.

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.