Evaluation of ensemble NWP models for dynamical downscaling of air temperature over complex topography in a hot climate: A case study from the Sultanate of Oman

This paper evaluates the use of ensemble numerical weather prediction (NWP) models for dynamical downscaling of temperature over a complex, hot region. This approach delivers information about the uncertainty of the NWP models and provides probabilistic information for comparison with the currently used single NWP model. An ensemble system was constructed using four members with a 7 km resolution over Oman. Two limited-area models (LAMs), the high-resolution model (HRM) and the model from the Consortium for Small-Scale Modeling (COSMO) formed the ensemble members. The two LAMs were derived and initialized using the general circulation model (GCM) data from the German Global Model (GME), which runs at 40 km resolution, using two different initial atmospheric states. The first initial state was provided by the 3Dvar data assimilation system at the German Weather Service (Deutscher Wetterdienst, DWD), and the second initial state was provided from the reanalysis data (ERA-Interim) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results reveal the uncertainty in temperature prediction related to the uncertainty of the NWP models that were used and indicate that there is no best model for the entire domain. On average, the ensemble mean performed better than individual members.