首页|期刊导航|气象学报（英文版）|TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts

TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts

Yunqing LIU Lu YANG Mingxuan CHEN Jianwei SI Maoyu WANG Wenyuan LI Jingfeng XU

气象学报（英文版）2025，Vol.39Issue(1)：59-78,20.

气象学报（英文版）2025，Vol.39Issue(1)：59-78,20.DOI:10.1007/s13351-025-4080-y

TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts

Yunqing LIU ¹Lu YANG ²Mingxuan CHEN ³Jianwei SI ⁴Maoyu WANG ⁴Wenyuan LI ⁴Jingfeng XU¹

作者信息

1. Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100||Institute of Urban Meteorology,China Meteorological Administration,Beijing 100089
2. Institute of Urban Meteorology,China Meteorological Administration,Beijing 100089
3. Institute of Urban Meteorology,China Meteorological Administration,Beijing 100089||Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science & Technology,Nanjing 210044
4. Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100
折叠

摘要

关键词

thunderstorm gusts/deep learning/interpretability/multisource data/weather forecasting

Key words

thunderstorm gusts/deep learning/interpretability/multisource data/weather forecasting

引用本文复制引用

Yunqing LIU,Lu YANG,Mingxuan CHEN,Jianwei SI,Maoyu WANG,Wenyuan LI,Jingfeng XU..TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts[J].气象学报（英文版）,2025,39(1):59-78,20.

基金项目

Supported by the National Key Research and Development Program of China(2022YFC3004103),Beijing Natural Science Founda-tion(8222051),China Meteorological Administration Key Innovation Team(CMA2022ZD04 and CMA2022ZD07),and Nanjing Joint Institute for Atmospheric Sciences Beijige Open Research Fund(BJG202407).The authors express their deep gratitude to the editors and anonymous reviewers,as well as to the Institute of Urban Meteorology for providing the pertinent radar,lightning,and automatic weather sta-tion data.Additionally,the authors acknowledge the in-valuable support from the Beijing Meteorological Ser-vice Data Centre in facilitating access to GPU comput-ing resources. （2022YFC3004103）

气象学报（英文版）

ISSN：0894-0525

访问量2

下载量0

段落导航