An effective lightning warning system can ensure the safety of aircraft and promote the development of a low-altitude economy.Compared with weather radars,ground-based atmospheric electric field mills can monitor elec...An effective lightning warning system can ensure the safety of aircraft and promote the development of a low-altitude economy.Compared with weather radars,ground-based atmospheric electric field mills can monitor electric field variations in low-altitude regions in real-time without being affected by ground clutter.To address current challenges in lightning warning methods based on atmospheric electric field data—such as limited lightning location samples and a high false alarm rate(FAR)—this thesis proposes a lightning warning model that integrates multi-station atmospheric electric field data with meteorological variables such as temperature and humidity,combined with data augmentation techniques.First,temporal and lagging features of the electric field are extracted and fused with multidimensional meteorological data including temperature,humidity,wind speed,and total cloud cover.A spatial-temporal density-based spatial clustering of applications with noise(ST-DBSCAN)is employed to annotate samples across multiple stations.The mode-normalized Wasserstein generative adversarial network with gradient penalty(MN-WGAN-GP)is used to generate synthetic samples with distributions similar to real data.Finally,a lightning warning algorithm is constructed based on categorical boosting(CatBoost).Experimental results show that the model achieves a probability of detection(POD)of 82.89%and a FAR of 27.33%on the test set.The proposed algorithm contributes to the development of refined regional lightning warning technologies and ensures the safety of low-altitude operations.展开更多
A lightning warning system （LWS） which can predict the possibility of lightning strike and the position of lightning discharge was developed. The LWS uses the electric field intensity at the ground as a parameter to...A lightning warning system （LWS） which can predict the possibility of lightning strike and the position of lightning discharge was developed. The LWS uses the electric field intensity at the ground as a parameter to warn the possibility of lightning strikes. A planar shutter type electric field mill （EFM） with a rotating vane is studied to measure the electric field. From a calibration experiment, the sensitivity of the EFM was adjusted to 0.15 V/kV/m, and this covers the ranges from 200 V/m to 20 kV/m. Magnetic field waveform is detected by a crossed loop coil and an integral amplifier. Frequency bandwidth of the circuit ranges from 5 kHz to 1.2 MHz. The polarity of lightning discharges is discriminated by electric field component. After fixing the polarity, we can calculate the direction and distance of lightning discharge by the peak and the zero cross time of the detected magnetic field waveform.展开更多
基金funded by the National Natural Science Foundation of China,grant number 41605121Foundation of Key Laboratory of Big Data&Artificial Intelligence in Transportation(Beijing Jiaotong University),Ministry of Education(No.BATLAB202402)。
文摘An effective lightning warning system can ensure the safety of aircraft and promote the development of a low-altitude economy.Compared with weather radars,ground-based atmospheric electric field mills can monitor electric field variations in low-altitude regions in real-time without being affected by ground clutter.To address current challenges in lightning warning methods based on atmospheric electric field data—such as limited lightning location samples and a high false alarm rate(FAR)—this thesis proposes a lightning warning model that integrates multi-station atmospheric electric field data with meteorological variables such as temperature and humidity,combined with data augmentation techniques.First,temporal and lagging features of the electric field are extracted and fused with multidimensional meteorological data including temperature,humidity,wind speed,and total cloud cover.A spatial-temporal density-based spatial clustering of applications with noise(ST-DBSCAN)is employed to annotate samples across multiple stations.The mode-normalized Wasserstein generative adversarial network with gradient penalty(MN-WGAN-GP)is used to generate synthetic samples with distributions similar to real data.Finally,a lightning warning algorithm is constructed based on categorical boosting(CatBoost).Experimental results show that the model achieves a probability of detection(POD)of 82.89%and a FAR of 27.33%on the test set.The proposed algorithm contributes to the development of refined regional lightning warning technologies and ensures the safety of low-altitude operations.
文摘A lightning warning system （LWS） which can predict the possibility of lightning strike and the position of lightning discharge was developed. The LWS uses the electric field intensity at the ground as a parameter to warn the possibility of lightning strikes. A planar shutter type electric field mill （EFM） with a rotating vane is studied to measure the electric field. From a calibration experiment, the sensitivity of the EFM was adjusted to 0.15 V/kV/m, and this covers the ranges from 200 V/m to 20 kV/m. Magnetic field waveform is detected by a crossed loop coil and an integral amplifier. Frequency bandwidth of the circuit ranges from 5 kHz to 1.2 MHz. The polarity of lightning discharges is discriminated by electric field component. After fixing the polarity, we can calculate the direction and distance of lightning discharge by the peak and the zero cross time of the detected magnetic field waveform.
