Case Analysis of Deep Learning Methods for Predicting Radar Echoes of Hail Clouds in Aksu Region
Utilizing the radar echo data from the hail cloud in the Aksu region from April to September in 2021-2022,a deep learning echo extrapolation model was jointly constructed based on the trajectory GRU model and generative adversarial networks(GAN)model.This model was applied for severe convective(hail)weather monitoring and early warning.An evaluation method based on different thresholds and forecast duration was employed to analyze the echo prediction performance of the deep learning model.Results indicate that:(1)Within 30 min forecast period,as the reflectivity threshold increases,both the Critical Success Index(CSI)and the Probability of Detection(POD)gradually decrease,while the False Alarm Rate(FAR)first decreases and then increases,reaching its lowest at the reflectivity threshold of 35 dBZ.(2)At the reflectivity threshold of 35 dBZ and the same extrapolation duration,compared to the optical flow method,the deep learning echo extrapolation model increases the CSI and POD by 0.05-0.15,and reduces FAR by 0.05-0.12.(3)In terms of predicting the movement path of severe convection cells at a reflectivity threshold of 35 dBZ,the deep learning echo extrapolation model,compared to the TITAN method,provides a prediction path that is closer to the actual movement path of the cells.
万方数据
维普
