- 1University of Bologna, Department of Physics and Astronomy, Bologna, Italy
- 2National Agency for Meteorology and Climatology, ItaliaMeteo, Bologna, Italy
- 3Arpae Emilia-Romagna, Bologna, Italy
- 4Katholische Universität Eichstätt - Ingolstadt, Mathematical Institute for Machine Learning and Data Science, Ingolstadt, Germany
Accurate representation of atmospheric dynamics at convection 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 the Kilometre-scale Ensemble Data Assimilation (KENDA) system, 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 positive impact of data assimilation at convection scale on the forecast of this event, which allows to improve the localization and the intensity of the storm although substantial underestimation of precipitation still persists. The relative impact of different observations datasets is evaluated, starting from conventional and radar data operationally assimilated for numerical weather predictions over Italy. After pointing out the importance of low-level moisture convergence in the process of convection initiation and the significant undersampling of humidity field in conventional data, the added value of humidity-sensitive microwave radiances from polar satellites is analyzed. Observations sensitive to mid-lower tropospheric humidity in clear-sky conditions are employed, taken from the Microwave Humidity Sounder instrument, still little investigated in limited-area models at many numerical weather prediction centers. In order to better exploit the information content of microwave satellite observations, the preliminary development towards all-sky assimilation is presented.
How to cite: Grenzi, M., Gastaldo, T., Poli, V., Marsigli, C., Janjic, T., Cacciamani, C., and Carrassi, A.: Assimilating multi-platform observations to improve severe convection forecasting in the ICON model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10874, https://doi.org/10.5194/egusphere-egu25-10874, 2025.