一种基于气团标签的锋面智能识别方法  

An Intelligent Identification Method of Fronts Based on Air Mass Labels

作　　者：丁新亚 李骞 汪天颖 张亮[1,3] 刘宇迪 张云鹏 黄兵 冯晓 DING Xinya;LI Qian;WANG Tianying;ZHANG Liang;LIU Yudi;ZHANG Yunpeng;HUANG Bing;FENG Xiao(College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China;Hunan Institute of Meteorological Sciences,Changsha 410118,China;Sichuan Meteorological Disaster Defense Technology Center,Chengdu 610072,China;Hunan Guotian Electronic Technology Co.,Ltd.,Changsha 410205,China)

机构地区：[1]国防科技大学气象海洋学院,湖南长沙410073 [2]湖南省气象科学研究所,湖南长沙410118 [3]四川省气象灾害防御技术中心,四川成都610072 [4]湖南国天电子科技有限公司,湖南长沙410205

出　　处：《热带气象学报》2024年第6期974-982,共9页Journal of Tropical Meteorology

基　　金：国家自然科学基金面上项目(42075139、U2242201、41305138);中国博士后科学基金会项目(2017M621700);湖南省自然科学基金(2021JC0009、2021JJ30773);风云应用创业项目(FY-APP2022.0605);高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金项目(SCQXKJYJXZD202406、SCQXKJQN202321、SCQXKJYJXMS202212)共同资助。

摘　　要：目前自动识别锋面的机器学习方法,因使用的锋面标签中有锋格点与无锋格点的比例严重不平衡,易导致训练的网络识别结果偏向无锋类别的问题,另外,网络输入的多气象要素,因特殊天气情况和地理位置等原因会产生数据特征冲突或者数据信息质量差的问题,造成输入数据与网络不匹配,后果是网络训练困难,识别准确度受影响。为此,提出一种基于气团标签训练AMA-UNet(自适应融合多气象要素U型网络)模型来进行锋面智能识别的方法,该方法使用欧洲中期天气预报中心的ERA5数据集的多个气象因子作为网络输入,将美国天气预报中心(WPC)提供的锋面数据集制作成气团标签以解决锋面标签中无锋类别与有锋类别的严重不平衡,同时利用AMA-UNet架构中的适配器解决输入数据与网络的不匹配问题,利于网络训练的同时,提高网络的综合性能。实验表明,气团作为标签比锋面直接作为标签训练的网络在评估指标上平均约提升5%,增加适配器后,网络在多评估指标上平均约提升3%,与其他方法相比各评估指标均有大幅提升。Current machine learning methods for the automatic identification of fronts often face challenges due to a serious imbalance in the proportion of frontal grid points versus non-frontal grid points in the training labels.This imbalance can lead to biased recognition results in favor of the non-frontal category.Moreover,the input of multiple meteorological elements may result in data feature conflicts or poor quality data due to special weather conditions and geographic variations,leading to mismatches between input data and the network.Consequently,this affects the training process and recognition accuracy.To address these issues,we proposed a method that trains the AMA-UNet model for the intelligent identification of fronts based on air mass labels.This approach used multiple meteorological parameters from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts as network inputs,while generating air mass labels from the front dataset provided by the Weather Prediction Center in the U.S.This effectively mitigated the imbalance between non-frontal and frontal categories.Furthermore,the adapter in the AMA-UNet architecture was utilized to resolve the mismatch between the input data and the network,which facilitated network training and improved the comprehensive performance of the network.Experiments show that the use of air masses as labels improved evaluation metrics by approximately 5%compared to networks trained directly using fronts as labels.Moreover,incorporating adapters yielded an average improvement of about 3%across multiple evaluation metrics.This method demonstrates significant enhancements in all evaluation indexes compared with other methods.

关 键 词：锋面自动识别 机器学习方法 气团标签 适配器 AMA-UNet 

分 类 号：P441[天文地球—大气科学及气象学]