Bayesian Model Verification of NWP Ensemble Forecasts

Abstract

orecasts of convective precipitation have large uncertainties. To consider the forecast uncertainties of convection-permitting models, a convection-permitting ensemble prediction system (EPS) based on the Consortium for Small-scale Modeling (COSMO) model with a horizontal resolution of 2.8 km covering all of Germany is being developed by the Deutscher Wetterdienst (DWD). The deterministic model is named COSMO-DE. Vertical structures of temperature and humidity affect the potential for convective instability. For verification of vertical model profiles, radiosonde data are used. However, the observed state is uncertain by itself because of the well-known limits in observing the atmosphere. In this work the authors use a probabilistic approach, which considers the observation error as well as the model uncertainty to validate multidimensional state vectors (e.g., temperature profiles) of the COSMO-DE-EPS and of two mesoscale ensembles with horizontal resolution of 10 km and parameterized convection. The mesoscale ensembles are the COSMO short-range EPS (COSMO-SREPS) and the COSMO limited-area EPS (COSMO-LEPS). The approach is based on Bayesian statistics and allows for both verification and comparison of ensembles. The investigation period comprises August 2007 for a comparison of the COSMO-DE-EPS with the COSMO-SREPS. A period of 5 days in July 2007 is used to demonstrate the potential of the Bayesian approach for verification by evaluating the COSMO-SREPS and COSMO-LEPS against COSMO-EU analyses. Based on the Bayesian approach, it is shown that the temperature profiles modeled by the COSMO-DE-EPS are more consistent with the observed profiles than those of COSMO-SREPS.