Effects of Large-Scale Constraint and Constraint Variables on the High-Frequency Assimilation of Radar Reflectivity Data in Convective Precipitation Forecasting

High-frequency cyclical assimilation of the retrieved rainwater and estimated in-cloud water vapor by radar reflectivity has positive impacts on convective precipitation forecasting but usually causes overestimation. The application of large-scale constraints will produce more balanced dynamical and thermal fields, which can address the above issue to some degree. In this study, the European Centre for Medium-Range Weather Forecasts (ECMWF) global forecast fields are utilized as large-scale constraints that are imposed on the regional model by the grid nudging method. Two heavy rainfall events that occurred in Jiangsu (the South case) and Hebei (the North case) Provinces with different water vapor background conditions are chosen. The results show that the experiment with dynamical constraints (nudging of the horizontal wind field only) performs the best 6-h precipitation location and intensity forecasts for both cases. The experiment that nudged the water vapor mixing ratio together with the horizontal wind field could significantly weaken the forecast precipitation intensity. Although it produces good precipitation forecasts in the first 3-h for the South case (under higher water vapor conditions), it produces an unreliable precipitation forecast with rapid decay for the North case. For the North case which is accompanied by significant cooling, the experiment nudging the water vapor mixing ratio, temperature and horizontal wind fields simultaneously performs better than the experiment nudging the water vapor mixing ratio together with the horizontal wind field.