Given the expected increase in the frequency and intensity of severe weather events due to global warming, improving weather forecasting capability in terms of both spatial resolution and lead times is a key factor for reducing extreme events impact. The climate of the Calabrian peninsula (southern Italy) is dominated by the interactions of the air masses with the surrounding Mediterranean Sea and strongly influenced by its complex steep orography, which often amplifies precipitation amounts worsening ground effects.
With the aim of investigating the capability of a state-of-the-art modelling chain to deliver accurate forecasts for civil protection purposes in the Calabria Region, an experimental high-resolution hydrometeorological modelling system has been developed recently at the Department of Environmental Engineering of the University of Calabria, providing forecasts up to the hydrological impact. The system is based on the Advanced Research WRF (ARW) mesoscale model in its version 3.9.1, with two one-way nested domains, the innermost having 2-km resolution. The boundary and initial conditions are provided operationally by the Global Forecasting System (GFS) in its high-resolution version and, for back-analysis purposes, by the European Centre for Medium-range Weather Forecasts’ Integrated Forecasting System (IFS). Finally, to simulate the hydrological impact of the atmospheric forcing, the WRF-Hydro 5.0 modelling system in a one-way mode with a horizontal resolution of 200 m is linked to the system and applied on all the main river networks of the region.
The accuracy and efficiency of the system have been tested with two events occurred in Autumn 2019. Though the synoptic conditions showed some significant differences, both the events affected mainly the central part of the region, causing about 230 mm and 200 mm of rainfall in 72 hours, on the 11-13 November 2019 and on the 24-26 November 2019, respectively. The analysis focused particularly on the predictability of the events, evaluating the forecast accuracy by considering lead times from one week early.
Preliminary results highlight the ability to forecasts the events well in advance, proved by the comparison of the simulated rainfall with the ground-based observations and the reproduction of the main hydrological signals in the basins affected by the events.