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一种基于气团标签的锋面智能识别方法

丁新亚 李骞 汪天颖 张亮 刘宇迪 张云鹏 黄兵 冯晓

热带气象学报2024,Vol.40Issue(6):974-982,9.
热带气象学报2024,Vol.40Issue(6):974-982,9.DOI:10.16032/j.issn.1004-4965.2024.086

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

丁新亚 1李骞 1汪天颖 2张亮 3刘宇迪 1张云鹏 1黄兵 4冯晓5

作者信息

  • 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. 国防科技大学气象海洋学院,湖南 长沙 410073||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
  • 5. 四川省气象灾害防御技术中心,四川 成都 610072
  • 折叠

摘要

Abstract

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

Key words

automatic identification of fronts/machine learning methods/air mass labels/adapter/AMA-UNet

分类

天文与地球科学

引用本文复制引用

丁新亚,李骞,汪天颖,张亮,刘宇迪,张云鹏,黄兵,冯晓..一种基于气团标签的锋面智能识别方法[J].热带气象学报,2024,40(6):974-982,9.

基金项目

国家自然科学基金面上项目(42075139、U2242201、41305138) (42075139、U2242201、41305138)

中国博士后科学基金会项目(2017M621700) (2017M621700)

湖南省自然科学基金(2021JC0009、2021JJ30773) (2021JC0009、2021JJ30773)

风云应用创业项目(FY-APP2022.0605) (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 (SCQXKJYJXZD202406、SCQXKJQN202321、SCQXKJYJXMS202212)

热带气象学报

OA北大核心CSTPCD

1004-4965

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