首页|Deep Learning-Based Automatic Identification of Gust Fronts from Weather Radar Data
Deep Learning-Based Automatic Identification of Gust Fronts from Weather Radar Data
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and pub-lic safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep learning-based automatic gust front identification algorithm is proposed in this study.The algorithm utilizes Mask Region-based Convolutional Neural Network(Mask R-CNN),a state-of-the-art instance segmentation model,trained on a large dataset of 2623 gust front samples from S-band weather radar volume scans in East China and the North China Plain between 2009 and 2016.Extensive data preprocessing and manual annotation are performed to prepare the training dataset.The optimized model achieves impressive performance on a test set of 604 samples,with a detection probability of 93.21％,a false alarm rate of 3.60％,a missed alarm rate of 6.79％,and a critical success index of 90.08％.The algorithm demonstrates robust identification capabilities across gust fronts of varying scales,types,and parent thunderstorm systems,highlighting its operational applicability.
国家科技期刊平台
NSTL
万方数据
