Multi‐Task Learning for Tornado Identification Using Doppler Radar Data

Author:

Xie Jinyang1ORCID,Zhou Kanghui2ORCID,Chen Haonan3ORCID,Han Lei1ORCID,Guan Liang2,Wang Maoyu1,Zheng Yongguang2ORCID,Chen Hongjin1,Mao Jiaqi1

Affiliation:

1. Faculty of Information Science and Engineering Ocean University of China Qingdao China

2. National Meteorological Center of China Beijing China

3. Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA

Abstract

AbstractTornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events.

Funder

National Natural Science Foundation of China

National Science Foundation

Publisher

American Geophysical Union (AGU)

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