Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0

Abstract

Abstract. We present the largest sensitivity study to date for cloud cover using the Weather Forecasting and Research model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large-ensemble framework, ESIAS-met (Ensemble for Stochastic Integration of Atmospheric Simulations). This work demonstrates the capability and performance of ESIAS for large-ensemble simulations and sensitivity analysis. The study takes an iterative approach by first comparing over 1000 combinations of microphysics, cumulus parameterization, planetary boundary layer (PBL) physics, surface layer physics, radiation scheme, and land surface models on six test cases. We then perform more detailed studies on the long-term and 32-member ensemble forecasting performance of select combinations. The results are compared to CM SAF (Climate Monitoring Satellite Application Facility) satellite images from EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The results indicate a high sensitivity of clouds to the chosen physics configuration. The combination of Goddard, WRF single moments 6 (WSM6), or CAM5.1 microphysics with MYNN3 (Mellor–Yamada Nakanishi Niino level 3) or ACM2 (Asymmetrical Convective Model version 2) PBL performed best for simulating cloud cover in Europe. For ensemble-based probabilistic simulations, the combinations of WSM6 and SBU–YLin (Stony Brook University Y. Lin) microphysics with MYNN2 and MYNN3 performed best.