Impact of Different Reanalysis Data and Parameterization Schemes on WRF Dynamic Downscaling in the Ili Region
Different reanalysis data and physical parameterization schemes for the Weather Research and Forecasting (WRF) model are considered in this paper to evaluate their performance in meteorological simulations in the Ili Region. A 72-hour experiment was performed with two domains at the resolution of 27 km with one-way nesting of 9 km. (1) Final Analysis (FNL) and Global Forecast System (GFS) reanalysis data (hereafter, WRF-FNL experiment and WRF-GFS experiment, respectively) were used in the WRF
... e used in the WRF model. For the simulation of accumulated precipitation, both the WRF-FNL (mean bias of 0.79 mm) and WRF-GFS (mean bias of 0.31 mm) simulations can display the main features of the general temporal pattern and geographical distribution of the observed precipitation. For the simulation of the 2-m temperature, the simulation of the WRF-GFS experiment (mean warm bias of 1.81 °C and correlation coefficient of 0.83) was generally better than that of the WRF-FNL experiment (mean cold bias of 1.79 °C and correlation coefficient of 0.27). (2) Thirty-six physical combination schemes were proposed, each with a unique set of physical parameters. Member 33 (with the smallest mean-metric of 0.53) performed best for the precipitation simulation, and member 29 (with the smallest mean-metric of 0.64) performed best for the 2-m temperature simulation. However, member 29 and 33 cannot be distinguished from the other members according to their parameterizations. For this domain, ensemble members that contain the Mellor–Yamada–Janjic (MYJ) boundary layer (PBL) scheme and the Grell–Devenyi (GD) cumulus (CU) scheme are recommended for the precipitation simulation. The Geophysical Fluid Dynamics Laboratory (GFDL) radiation (RA) scheme and the MYJ PBL scheme are recommended for the 2-m temperature simulation.