New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing
The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1766 |
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| author | Bingzhe Dai Qilin Zhang Jie Li Yi Liu Minhong Zhao |
| author_facet | Bingzhe Dai Qilin Zhang Jie Li Yi Liu Minhong Zhao |
| author_sort | Bingzhe Dai |
| collection | DOAJ |
| description | The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly dependent on the quality of the training dataset. To address this, this paper proposes a method called Tri-Pre to improve dataset quality and thereby enhance the performance of single-site cloud-to-ground lightning detection based on deep learning. After using the Tri-Pre method, the location model’s distance estimation error decreases by 36.08%. For lightning with propagation distances greater than 1000 km, the average relative error of the results from the built model based on the Tri-Pre method is 3.78%. When verified using additional measured data, the model also shows satisfactory accuracy, particularly for lightning with propagation distances beyond 1000 km. Specifically, for lightning with propagation distances between 1500 and 1600 km, the average relative location error is approximately 5.46%. |
| format | Article |
| id | doaj-art-d0a2af9ca6d44218b8bb0d36b5374021 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d0a2af9ca6d44218b8bb0d36b53740212025-08-20T02:33:48ZengMDPI AGRemote Sensing2072-42922025-05-011710176610.3390/rs17101766New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre ProcessingBingzhe Dai0Qilin Zhang1Jie Li2Yi Liu3Minhong Zhao4Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThe single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly dependent on the quality of the training dataset. To address this, this paper proposes a method called Tri-Pre to improve dataset quality and thereby enhance the performance of single-site cloud-to-ground lightning detection based on deep learning. After using the Tri-Pre method, the location model’s distance estimation error decreases by 36.08%. For lightning with propagation distances greater than 1000 km, the average relative error of the results from the built model based on the Tri-Pre method is 3.78%. When verified using additional measured data, the model also shows satisfactory accuracy, particularly for lightning with propagation distances beyond 1000 km. Specifically, for lightning with propagation distances between 1500 and 1600 km, the average relative location error is approximately 5.46%.https://www.mdpi.com/2072-4292/17/10/1766single-site lightning locationpreprocessing methodlightning waveformdeep learning |
| spellingShingle | Bingzhe Dai Qilin Zhang Jie Li Yi Liu Minhong Zhao New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing Remote Sensing single-site lightning location preprocessing method lightning waveform deep learning |
| title | New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing |
| title_full | New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing |
| title_fullStr | New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing |
| title_full_unstemmed | New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing |
| title_short | New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing |
| title_sort | new method for single site cloud to ground lightning location based on tri pre processing |
| topic | single-site lightning location preprocessing method lightning waveform deep learning |
| url | https://www.mdpi.com/2072-4292/17/10/1766 |
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