基于R2CNN的天气雷达边界层辐合线识别算法  

Boundary Layer Convergence Line Identification Algorithm for Weather Radar Based on R2CNN

作　　者：郑玉 徐芬 王亚强[3] Zheng Yu;Xu Fen;Wang Yaqiang(Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041;Key Laboratory of Transportation Meteorology,CMA,Nanjing 210041;State Key Laboratory for Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081)

机构地区：[1]南京气象科技创新研究院,南京210041 [2]中国气象局交通气象重点开放实验室,南京210041 [3]中国气象科学研究院灾害天气国家重点实验室,北京100081

出　　处：《应用气象学报》2024年第6期654-666,共13页Journal of Applied Meteorological Science

基　　金：中国自然科学基金委员会气象联合基金项目(U2142203);中国气象科学研究院基本科研业务费专项资金(2021Z003,2023Z017);中国气象局重点创新团队(CMA2022ZD07)。

摘　　要：边界层辐合线是触发对流的中尺度天气系统之一,边界层辐合线的精细化识别对于揭示其形成、演变及与其他系统相互作用机制至关重要。目前自动识别技术在适应边界层辐合线多样性(如尺度、强度和形状)方面存在局限。旋转区域卷积神经网络(R2CNN)可提高识别准确性、鲁棒性和泛化能力。综合考虑天气雷达型号和分辨率的多样性,针对性构建识别数据集用于模型训练,调整相应参数得到识别模型,并利用交并比和置信度评估检验识别效果。结果表明:基于R2CNN的边界层辐合线识别算法在使用较低交并比阈值时命中率更高且空报率更低,当置信度为0.7时,TS(threat score)评分最高。与现有的阵风锋识别算法(Machine Intelligence Gust Front Algorithm,MIGFA)效果相比,R2CNN在减少误报、提升命中率及平衡识别频率等关键性能方面优势显著,适用于业务应用与推广。Boundary layer convergence lines are recognized as one of the critical mesoscale weather systems triggered convection,and also affect low-altitude flight safety.The accurate and detailed identification of these lines is considered essential for revealing their formation,evolution,and interaction mechanisms with other weather systems.However,existing automatic identification technologies are limited in their ability to adapt the diverse characteristics of these lines,such as scale,intensity,and shape.The rotational regionbased convolutional neural network(R2CNN)is employed to enhance the accuracy,robustness,and generalization of the identification process.A comprehensive identification dataset has been constructed for model training,considering the diversity of weather radar models and resolutions.Relevant parameters are adjusted to derive the optimized recognition model.The intersection over union(IoU)with confidence levels are employed to comprehensively assess and validate the identification results.Results indicate that the boundary layer convergence line recognition algorithm developed achieves a higher hit rate and a lower false alarm rate at lower IoU thresholds.At a confidence level of 0.7,the threat score(TS)reaches its maximum value.Compared to the existing Machine Intelligence Gust Front Algorithm(MIGFA),the model proposed in this study demonstrates significant advantages in reducing false alarms,improving hit rates,and achieving a balanced recognition frequency.Therefore,it is more suitable for operational applications and dissemination.This research not only provides a more effective method for identifying boundary layer convergence lines but also contributes to the improvement of low-altitude flight safety and advances meteorological detection technologies.The proposed method addresses limitations of existing technologies by effectively managing the diverse characteristics of boundary layer convergence lines.By incorporating rotational bounding boxes in the detection process,R2CNN model enhances the

关 键 词：边界层辐合线 低空飞行安全 阵风锋 R2CNN 旋转框目标检测 

分 类 号：P412.25[天文地球—大气科学及气象学]