Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

Abstract

Abstract A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012. The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread. Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.