Improving satellite radiance assimilation at convective-scale: from operational numerical weather predictions towards machine learning limited area models

Accurate representation of atmospheric dynamics at convection scale remains a major challenge for numerical models and a critical aspect in operational weather predictions. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with the Local Ensemble Transform Kalman Filter (LETKF), following the operational configuration of Arpae Emilia-Romagna and ItaliaMeteo Agency. We focus on a poorly-predicted extreme convective storm in the Marche region, Italy, highlighting the crucial role of low-level moisture convergence in convection initiation and the significant undersampling of humidity in conventional data. To address this, we investigate the added value of humidity-sensitive microwave radiances from polar satellites. Assimilation of clear-sky observations from the Microwave Humidity Sounder (MHS) leads to notable improvements in precipitation forecasts compared to the current operational setup, based on conventional and radar observations only. Infrared all-sky radiances in water vapor channels from the geostationary Meteosat Second Generation SEVIRI instrument are further integrated, providing higher spatial and temporal resolution but limited cloud penetration capability. The joint assimilation of microwave and infrared satellite channels leads to improvements in both surface and upper-level variables, supporting the future operational assimilation of satellite radiances at convective-scale in the Arpae and ItaliaMeteo system. The relative contribution of each observation type is evaluated through an updated version of the Partial Analysis Increments algorithm (Diefenbach et al., 2023), which is corrected to account for posterior covariance inflation and localization.

Building on the promising results of this work, we present ongoing developments towards ensemble-based data assimilation in a Machine Learning Limited Area Model (ML-LAM). Machine learning weather prediction models have demonstrated competitive forecast skill at coarse resolution, but convective-scale ML-LAMs remain much less explored. A recent study (Adamov et al., 2025) presents the development of a high-resolution ML-LAM, trained on a convective-scale analyses dataset over Switzerland. The potential reproducibility of ML-LAM across different regions makes it appealing for application in other LAM settings. We show here the implementation of ML-LAM over the Italian domain, evaluating the skill against the physics-based model in severe convection conditions. This serves as a first step towards the assimilation of satellite microwave radiances in a operational-like setting using an LETKF scheme.

 

Diefenbach, T., Craig, G., Keil, C., Scheck, L. and Weissmann, M., QJRMS, 149(752), 740–756, (2023).

 

Adamov, S., Oskarsson, J., Denby, L., Landelius, T., Hintz, K., Christiansen, S., Schicker, I., Osuna, C., Lindsten, F., Fuhrer, O. and Schemm,
S., arXiv, (2025).