Towards the assimilation of satellite radiances in the convection-permitting ICON model

Accurate representation of atmospheric dynamics at convective scale still represents a major challenge for numerical models and a critical aspect in operational weather predictions. Reliable forecast initial conditions, generated by the data assimilation cycle using new observations coming from different platforms, are crucial to improve the forecast accuracy in deep convection environments. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with a state-of-the-art ensemble data assimilation scheme to test the model performance on a poorly predicted extreme convective storm in the Marche region, Italy, on 15 September 2022. We show here the large positive impact of conventional and radar data assimilation on the forecast of this event, although substantial underestimation of precipitation still persists. The triggers of the storm are also investigated, pointing out the importance of low-level moisture convergence and topography in the process of convection initiation. The results of this study suggest further developments towards the assimilation of more widespread humidity-sensitive data, especially coming from satellite microwave radiances which are still little investigated in Limited Area Models.