Radar based rainfall nowcasting and its characteristic prediction based on spatially correlated random field, normalized duration line and Kalman filter algorithm

Rainfall is not only one of the most natural processes on the earth, but also an important factor of flood generation. Precise rainfall nowcasting can give an effective warning before hazards occur. This paper presented an ensemble nowcasting methodology which combined two deterministic nowcasting methods: PIV_Semi-Lagrangian and PIV_Lagrangian-Persistence and the spatial correlated random error field. For the deterministic nowcasting methods, the past velocity fields were estimated by Particle Image Velocimetry (PIV) method and the advection fields were extrapolated by Semi-Lagrange and Lagrange-Persistence schemes separately, then the forecasted errors at former time step were simulated by the spatially correlated random error field and were added to the next forecasting steps. Additionally, a predicting method for rain field property was proposed and a Kalman filter algorithm was also implemented for rain field’s centre of mass prediction. The methodology was applied to 8 historical rainfall events occurred in North Rhine Westphalia (NRW), Germany by using high-resolution rainfall data acquired from C-band Essen radar belonging German Weather Service (DWD). Results showed that the promoted ensemble nowcasting methods and the rain field property predicting methods improved the forecasting accuracy obviously which confirmed their effectiveness.