基于深度学习模型的雷电落区预报OA北大核心CSTPCD

In this study,Lightning-Net,a deep learning model for lightning strike location prediction,was developed using a PredRNN spatio-temporal prediction model as the backbone network,and an Astrus spatial pyramid pooling module as the classifier.The model employed the radar composite reflectivity and the lightning location data of the preceding hour as predictive factors to predict the lightning strike location for the subsequent hour.The model was trained with the radar composite reflectivity and the lightning location data of Chongqing during 2020-2021 and tested with the dataset of Chongqing in 2022.The results show that the Lightning-Net model,with a threat score of 0.53 and a probability of detection of 0.82,demonstrated advantages over traditional optical flow methods and U-Net models.Case studies show that the model's forecasting performance for severe thunderstorms was better than that for weak thunderstorms.While the model adeptly captured the overall trend of lightning strike location changes,it exhibited limitations in predicting sporadic lightning around the main body of thunderstorms.