Deep learning has been developed at an unprecedend speed in radar extrapolation of forecasting, so objective assessment of its applicability is an important prerequisites for operational applications. By utilizing the radar echo open data set of Guangdong-Hong Kong-Macao Greater Bay Area, the performances of 120 min radar echo extrapolation 〖JP2〗by convolutional gated recurrent unit neural network (ConvGRU) and fast dense optical flow (OF) methods based on semi-lagrangian advection scheme have been compared and evaluated based on the echo morphology, probability of detection (POD), false alarm rate (FAR) and threat score (TS). The results show that although the two methods both have effective extrapolation performance, they are not applicable to extrapolate the echo generation, enhancement and locally dispersed.