Forecast of high-impact weather over Italy: performance of global and limited-area ensemble prediction systems

The aim of this work is to assess the added value of the enhanced horizontal resolution in the probabilistic prediction of upper-level and surface fields. In particular, the performance of three different ensemble prediction systems were compared: ECMWF-ENS (51 members, 18 km horizontal resolution), COSMO-LEPS (16 members, 7 km horizontal resolution) and COSMO-2I-EPS (10 members, 2.2 km horizontal resolution). While the first 2 ensemble systems are operational, COSMO-2I-EPS is still in a development phase. Therefore, the intercomparison window covers a limited period, which ranges from 20 to 27 June 2016. In this work, both upper-level and surface variables are analyzed. As for upper-level, both temperature and the geopotential height at three different pressure levels are considered; the ensemble spread and the root mean square error are computed using the available Italian radiosounding data every 12/24 hours for verification. As for the surface, 2-metre temperature and precipitation cumulated over six hours are verified against the non-conventional station network provided by the National Civil Protection Department. The ensemble spread and the root mean square error of 2-metre temperature are computed, while a number of probabilistic scores (Brier Skill Score, Ranked Probability Score, Roc-Area, Outliers Percentage and others) are considered for precipitation. For both upper-level and surface verification, it turns out that the best scores are mainly obtained by the COSMO-based ensemble systems with higher horizontal resolution and lower ensemble size. The added value of high resolution in mesoscale ensemble systems seems to play a crucial role in the probabilistic prediction of atmospheric fields at all levels. In particular, the more detailed description of mesoscale and orographic-related processes in COSMO-ensembles provides an added value for the prediction of localised High-Impact Weather events.