Evaluation of the WRF physics ensemble using a multivariable integrated evaluation approach over the Haihe river basin in northern China

A crucial step in the application of the Weather Research and Forecasting (WRF) model to regional climate research is the selection of the proper combinations of physical parameterizations. In this study, we examined the performance of various parametrization schemes in the WRF model in terms of precipitation and temperature over the Haihe river basin in northern China. The WRF experiments were integrated with 13-km horizontal resolution and driven by ERA-INTERIM reanalysis data over the period from 1st June to 31st August, 2016. Fifty-eight members of physics combinations derived from five types of physics options were assessed against the available observational temperature and precipitation data by utilizing the multivariable integrated evaluation (MVIE) method. Our results indicated that the best combination of physical schemes consisted of CAM5.1 microphysics, MRF PBL, BMJ cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. The optimal setup’s differences with the observational data, temporally and spatially, were much smaller than other setups in terms of surface air temperature and precipitation, which proves that the optimal setup showed better performance than the other setups. Further analysis of the sensitivities of model outputs to different types of physics options suggests that the microphysics, planetary boundary layer (PBL), and cumulus schemes have a more significant impact on the model performances than the radiation scheme and Land Surface schemes.