Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members

Abstract

Abstract Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional convection permitting ensembles (RCPEs). RCPEs demand significant computer resources and often a limited number of ensemble members is affordable, which is smaller than the size of the driving GE. Since each RCPE member obtains the initial and boundary conditions from a specific GE member, there are many options to select the GE members. The study uses the European Centre for Medium‐Range Weather Forecasts (ECMWF) GE consisting of 50 members, to drive 20 members of COSMO model RCPE over the Eastern Mediterranean. We compare various approaches for automatic selection of the GE members and propose several optimal methods, including a random selection, which consistently lead to a better performance of the driven RCPE. The comparison includes verification of near surface variables and precipitation using various verification metrics. The results are validated using several methods of model physics perturbation. Besides the selection of the optimal ensemble configurations, we show that at high precipitation intensities spatial up‐scaling is recommended in order to obtain useful probabilistic forecasts.

Keywords

• ensemble
• NWP
• probabilistic forecast
• spread skill ratio