Impacts of the radar data assimilation frequency and large-scale constraint on the short-term precipitation forecast of a severe convection case

Abstract Radar data assimilation (DA) can improve short-term precipitation forecasts. However, assimilating radar observations into a numerical prediction model may produce spurious precipitation and large errors in the position and magnitude of precipitation, and high-frequency cyclic assimilation may exacerbate this problem. In this paper, a severe convection case that occurred on 27 June 2018 over eastern China was used to investigate the impacts of the radar DA frequency and large-scale constraint on precipitation forecasts. The Weather Research and Forecasting (WRF) three-dimensional variational (3D-Var) DA system was used to assimilate radial velocity and reflectivity data. The sensitivity of the DA frequency using 15-min and 1-h assimilation intervals was examined first, revealing that the 15-min DA interval produced a greater overprediction bias of precipitation. A diagnosis of the analysis fields showed that higher-frequency DA cycling produced excessive wind speed and water vapor convergence at low levels and exaggerated upward movement. Then, the large-scale constraint was imposed on the regional model by the spectral nudging (SN) technique, which assimilated Global Forecast System (GFS) forecast field data during the forecast periods. The results demonstrated that the application of SN could significantly reduce the positional deviation of the precipitation forecast and the magnitude of overpredicted precipitation. SN could effectively adjust large-scale circulation fields, thereby improving the conditions of water vapor convergence. Moreover, SN also improved the forecasts of surface variables such as wind, temperature, and humidity.