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Low-level wind shear classification for airborne weather radar based on signal simulation and residual network.

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Title: Low-level wind shear classification for airborne weather radar based on signal simulation and residual network.
Authors: Jin, Wenrui1,2 (AUTHOR) wrjin@tongji.edu.cn, Fang, Min2 (AUTHOR) 2330462@tongji.edu.cn, Lv, Xiaoxiao3 (AUTHOR) lvxiaoxiao2022@tongji.edu.cn, Li, Jiaxue2 (AUTHOR) 2210430@tongji.edu.cn, Zhang, Tao2 (AUTHOR) 2132660@tongji.edu.cn
Source: Advances in Space Research. Jun2026, Vol. 77 Issue 12, p11784-11800. 17p.
Subjects: Microbursts, Radar signal processing, Machine learning, Vertical wind shear, Artificial neural networks, Radar meteorology, Classification, Computer simulation
Abstract: Low-level wind shear (LLWS) is a sudden and hazardous meteorological phenomenon that seriously threatens aircraft safety. Conventional detection methods rely on parameter calibration for identification but lack detailed classification capability. Additionally, the dynamic complexity of atmospheric wind fields and the substantial volume of raw radar data impose high acquisition risks and storage constraints onboard. To address these challenges, this study proposes a simulation-based approach for classifying airborne weather radar-identified LLWS during takeoff and landing. The method first establishes four typical LLWS field models—microburst, crosswind, low-level jet, and sea–land breeze—based on fluid dynamics principles. It then simulates the aircraft takeoff trajectory using a standard race-track pattern and generates airborne radar echoes via MATLAB integrated with a wind field retrieval algorithm to produce radial velocity images. A comprehensive dataset of wind shear images under varied conditions is constructed. Finally, a residual network integrated with transfer learning is developed, which achieves a classification accuracy of 95.50% across six categories and outperforms both Inception V1 and SqueezeNet in experimental trials, demonstrating a promising solution for LLWS classification using airborne radar. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 194127382
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Low-level wind shear classification for airborne weather radar based on signal simulation and residual network.
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  Data: <searchLink fieldCode="AR" term="%22Jin%2C+Wenrui%22">Jin, Wenrui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wrjin@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Min%22">Fang, Min</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 2330462@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lv%2C+Xiaoxiao%22">Lv, Xiaoxiao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> lvxiaoxiao2022@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jiaxue%22">Li, Jiaxue</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 2210430@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tao%22">Zhang, Tao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 2132660@tongji.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Space+Research%22">Advances in Space Research</searchLink>. Jun2026, Vol. 77 Issue 12, p11784-11800. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Microbursts%22">Microbursts</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+signal+processing%22">Radar signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Vertical+wind+shear%22">Vertical wind shear</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+meteorology%22">Radar meteorology</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink>
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  Label: Abstract
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  Data: Low-level wind shear (LLWS) is a sudden and hazardous meteorological phenomenon that seriously threatens aircraft safety. Conventional detection methods rely on parameter calibration for identification but lack detailed classification capability. Additionally, the dynamic complexity of atmospheric wind fields and the substantial volume of raw radar data impose high acquisition risks and storage constraints onboard. To address these challenges, this study proposes a simulation-based approach for classifying airborne weather radar-identified LLWS during takeoff and landing. The method first establishes four typical LLWS field models—microburst, crosswind, low-level jet, and sea–land breeze—based on fluid dynamics principles. It then simulates the aircraft takeoff trajectory using a standard race-track pattern and generates airborne radar echoes via MATLAB integrated with a wind field retrieval algorithm to produce radial velocity images. A comprehensive dataset of wind shear images under varied conditions is constructed. Finally, a residual network integrated with transfer learning is developed, which achieves a classification accuracy of 95.50% across six categories and outperforms both Inception V1 and SqueezeNet in experimental trials, demonstrating a promising solution for LLWS classification using airborne radar. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.asr.2026.04.015
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 11784
    Subjects:
      – SubjectFull: Microbursts
        Type: general
      – SubjectFull: Radar signal processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Vertical wind shear
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Radar meteorology
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Computer simulation
        Type: general
    Titles:
      – TitleFull: Low-level wind shear classification for airborne weather radar based on signal simulation and residual network.
        Type: main
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            NameFull: Jin, Wenrui
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            NameFull: Fang, Min
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            NameFull: Lv, Xiaoxiao
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            NameFull: Li, Jiaxue
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            NameFull: Zhang, Tao
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 77
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