A study on radar echo extrapolation nowcasting method combined with physical constraints of mesoscale model

This study develops an intelligent extrapolation nowcasting method combined with physical constraints of mesoscale numerical model. The method uses the high-resolution mesoscale model information to constrain the extrapolation within 2 h. Based on optical flow (OF) method extrapolation, the dynamic constraint extrapolation optical flow (DCOF) method is applied to reconstruct the motion vector; the mesoscale numerical model diagnostic products and radar historical data are used to train the echo intensity frequency prediction model, which integrates multiple deep learning algorithms through the voting regressor. Finally, a physical constraint optical flow (PCOF) method is used to correct the original echo intensity of the OF method by using the frequency-matching method. The performance of the OF method, DCOF method and PCOF method in 2 typical cases and cases over a long-term period are evaluated. The results show that the DCOF method improves the prediction by improving the unrealistic accumulation and distortion produced by the OF method at the edge of the echoes. The PCOF method improves the prediction by adjusting the echo intensity frequency distribution prediction based on the model information. With increasing forecast lead time, the improvements caused by the two methods are becoming more obvious. Overall, the PCOF method is the best among the three methods.