  We investigate the potential added value of running three limited-area
ensemble systems (with the WRF, Meso-NH and MOLOCH models and a grid spacing of
approximately 2.5 km) for two heavy-precipitation events in Italy. Such
high-resolution ensembles include an explicit treatment of convective processes
and dynamically downscale the ECMWF global data, which have a grid spacing of
approximately 18 km. The predictions are verified against rain-gauge data and
their accuracy is evaluated over that of the driving coarser-resolution
ensemble system. Furthermore, we compare the simulation speed (defined as the
ratio of simulation length to wall-clock time) of the three limited-area models
to estimate the computational effort for operational convection-permitting
ensemble forecasting. We also study how the simulation wall-clock time scales
with increasing numbers of computing elements (from 36 to 1152 cores).
Objective verification methods generally show that convection-permitting
forecasts outperform global forecasts for both events, although precipitation
peaks remain largely underestimated for one of the two events. Comparing
simulation speeds, the MOLOCH model is the fastest and the Meso-NH is the
slowest one. The WRF model attains efficient scalability, whereas it is limited
for the Meso-NH and MOLOCH models when using more than 288 cores. We finally
demonstrate how the model simulation speed has the largest impact on a joint
evaluation with the model performance because the accuracy of the three
limited-area ensembles, amplifying the forecasting capability of the global
predictions, does not differ substantially.
