C-Laef: Convection-Permitting Limited-Area Ensemble Forecasting System

C-LAEF (Convection-permitting Limited-Area Ensemble Forecasting) has been developed at the Austrian national weather service ZAMG (Zentralanstalt fur Meteorologie und Geodynamik) and has been running operationally at the European Centre for Medium Range Weather Forecasts (ECMWF) supercomputer since November 2019. It includes (a) an ensemble 3D variational blending technique to deal with atmospheric initial uncertainties, (b) an ensemble of land surface data assimilation to account for uncertainties in the initial land surface conditions, (c) a hybrid stochastic physics perturbation scheme to treat model uncertainties in the different physics parametrization schemes, and (d) a coupling with the global ensemble system IFS-ENS (Integrated Forecasting System-ENSemble) to consider uncertainties in the lateral boundary conditions. C-LAEF has a horizontal resolution of 2.5 km and consists of 16 perturbed members plus one unperturbed control run. It runs four times a day and provides probabilistic forecasts up to 60 hr on a domain covering the whole Alpine area. This article describes the C-LAEF system in detail and evaluates the relative contributions of the different perturbation techniques. The ensemble variational blending technique and the ensemble surface data assimilation provide additional spread in the first forecast hours, while the hybrid stochastically perturbed parametrization scheme improves the performance of C-LAEF during the whole forecast range. The performance of C-LAEF is evaluated extensively over one summer and one winter period and compared with its mesoscale counterpart ALADIN-LAEF (Aire Limitee Adaptation dynamique Developpement InterNational - Limited-Area Ensemble Forecasting) and the IFS-ENS. State-of-the-art probabilistic measures indicate that C-LAEF is able to outperform ALADIN-LAEF for all considered upper-air variables. At the surface, C-LAEF outperforms ALADIN-LAEF and IFS-ENS according to most conventional measures, particularly for wind and precipitation. C-LAEF benefits from the higher resolution and the explicit treatment of deep convection and can provide more accurate probabilistic information for weather warnings.