A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal
Abstract Most real‐time lightning location systems are based on feature matching to locate lightning, but they often lack the ability to locate lightning channels. To achieve lightning channel location based on feature matching, a new location algorithm is proposed by utilizing time series segmented...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-03-01
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| Series: | Earth and Space Science |
| Online Access: | https://doi.org/10.1029/2024EA003896 |
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| _version_ | 1850208188433956864 |
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| author | Jingxuan Wang Yang Zhang Yanfeng Fan Yijun Zhang Dong Zheng Weitao Lyu |
| author_facet | Jingxuan Wang Yang Zhang Yanfeng Fan Yijun Zhang Dong Zheng Weitao Lyu |
| author_sort | Jingxuan Wang |
| collection | DOAJ |
| description | Abstract Most real‐time lightning location systems are based on feature matching to locate lightning, but they often lack the ability to locate lightning channels. To achieve lightning channel location based on feature matching, a new location algorithm is proposed by utilizing time series segmented feature to match lightning pulses. The features of waveform time series do not require complex signal processing, making it suitable for real‐time and fast location. Compared with the location results of the other three existing methods for a lightning event, the new method achieves the highest matching efficiency of 28.4% and demonstrates fine channel location capability. For a thunderstorm process, the new method also has the highest location efficiency, as well as the highest number of valid location points per second, and the lowest computation time of per valid location point, which are 26%, 39.6, and 0.025s, respectively. The new algorithm also provides better location results for irregular pulse clusters, which more realistically depict the development process of downward leader compared to the location methods based on encoding feature matching. This may be caused by the fact that the time series segmented feature can correctly represent the change trend of the signal under the condition of low signal‐to‐noise ratio. |
| format | Article |
| id | doaj-art-7c451beafedc4ed08376895ca97a641a |
| institution | OA Journals |
| issn | 2333-5084 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-7c451beafedc4ed08376895ca97a641a2025-08-20T02:10:17ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842025-03-01123n/an/a10.1029/2024EA003896A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency SignalJingxuan Wang0Yang Zhang1Yanfeng Fan2Yijun Zhang3Dong Zheng4Weitao Lyu5State Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing ChinaDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan University Shanghai ChinaState Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing ChinaAbstract Most real‐time lightning location systems are based on feature matching to locate lightning, but they often lack the ability to locate lightning channels. To achieve lightning channel location based on feature matching, a new location algorithm is proposed by utilizing time series segmented feature to match lightning pulses. The features of waveform time series do not require complex signal processing, making it suitable for real‐time and fast location. Compared with the location results of the other three existing methods for a lightning event, the new method achieves the highest matching efficiency of 28.4% and demonstrates fine channel location capability. For a thunderstorm process, the new method also has the highest location efficiency, as well as the highest number of valid location points per second, and the lowest computation time of per valid location point, which are 26%, 39.6, and 0.025s, respectively. The new algorithm also provides better location results for irregular pulse clusters, which more realistically depict the development process of downward leader compared to the location methods based on encoding feature matching. This may be caused by the fact that the time series segmented feature can correctly represent the change trend of the signal under the condition of low signal‐to‐noise ratio.https://doi.org/10.1029/2024EA003896 |
| spellingShingle | Jingxuan Wang Yang Zhang Yanfeng Fan Yijun Zhang Dong Zheng Weitao Lyu A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal Earth and Space Science |
| title | A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal |
| title_full | A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal |
| title_fullStr | A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal |
| title_full_unstemmed | A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal |
| title_short | A Fast and Fine‐Resolution Location Method for Lightning Channels Based on Time Series Segmented Feature of Low Frequency Signal |
| title_sort | fast and fine resolution location method for lightning channels based on time series segmented feature of low frequency signal |
| url | https://doi.org/10.1029/2024EA003896 |
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