热带气象学报2024，Vol.40Issue(5)：758-766,9.DOI:10.16032/j.issn.1004-4965.2024.068
基于深度学习模型的雷电落区预报
Lightning Strike Location Prediction Using a Deep Learning Model
摘要
Abstract
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.关键词
深度学习/雷电落区预报/雷达/雷电定位数据Key words
deep learning/lightning strike location prediction/radar/lightning location data分类
天文与地球科学引用本文复制引用
任照环,林锐,周浩,覃彬全,李卫平,许伟..基于深度学习模型的雷电落区预报[J].热带气象学报,2024,40(5):758-766,9.基金项目
重庆市科委技术创新与应用示范项目:爆炸危险场所雷电防护关键技术研究(cstc2018jscx-msybX0137) （cstc2018jscx-msybX0137）
重庆市气象部门业务技术攻关项目:重庆混合基线闪电定位资料的应用(YWJSGG-202147)共同资助 （YWJSGG-202147）
