A study of three-dimensional radar reflectivity mosaic assimilation in the regional forecasting model for North China

The three-dimensional (3D) radar reflectivity mosaic covering North China is assimilated into the regional numerical weather forecasting system RMAPS-ST via an indirect radar reflectivity assimilation method of WRF-3DVAR to improve the model forecasting skill with focuses on the influences on precipitation, specific humidity, temperature and wind forecasting. Firstly, experiments with/without radar reflectivity mosaic assimilation have been performed from 1 to 31 August 2017, and quantitative verification is conducted based on the batch experiments. The results show that the radar reflectivity mosaic assimilation significantly improves the skill for precipitation, specific humidity and temperature forecasting but increases the forecasting error in the wind field. Secondly, how the radar reflectivity mosaic assimilation improves the forecasting skill of RMAPS-ST is displayed based on a heavy rainfall case, which shows:(1) Precipitation forecasting skill is greatly improved and the cycle that is closer to the beginning of rainfall has higher forecasting skill by assimilating radar mosaic. (2) Water vapor, energy and thermal condition which are consumed and weakened by the previous rainfall can be boosted and re-organized to trigger new rainfall by assimilating radar mosaic reflectivity in the cycling way, which plays an important role in the situation of multiple rainfalls occurring during a short time period. Finally, two schemes of the WRF-3DVAR indirect radar reflectivity assimilation method are tested. Results indicate that the retrieved hydrometeor assimilation scheme and the derived water vapor assimilation scheme both can improve precipitation forecasting, but the latter one plays a more important role and using two schemes together can make reasonable adjustments for the rainwater, snow water, water vapor and thermal condition, which are critical for the improvement of precipitation forecasting.