Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Junchao Zhang has a total of 15 co-authors. Here are the top co-authors by number of shared publications:

  • Bingzhe Dai

    Collaborative 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, China

    4 shared publications
  • Jiahao Zhou

    Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory for Aerosol Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

    4 shared publications
  • Jie Li

    Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory for Aerosol Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

    4 shared publications
  • Qilin Zhang

    Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China

    4 shared publications
  • Yao Wang

    Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory for Aerosol Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

    4 shared publications
  • Jiaying Gu

    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Key Laboratory for Aerosol Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

    3 shared publications

Latest Publications

  • Open Access

    Effect of Number and Configuration of Participating Stations on Lightning Location outside the Network

    by Jiaying Gu, Qilin Zhang, Jie Li, Junchao Zhang, Jiahao Zhou, Bingzhe Dai, Hao Sun, Yao Wang, Jialei Wang, Yuqing Zhong, et al.

    The effect of the number and configuration of participating stations on lightning location outside the network is herein studied by evaluating the deviation distance between the reference location and the locations determined by the ToA technique, using recorded data from the location network in Fujian. It was found that the deviation distance decreases with the increase of number of stations, changing from 0.07 to 424.7 km with an average of 35 km for five stations while being 0.03~21.6 km and 2.2 km, respectively, for eight stations. The spatial distribution of those locations outside the network seems to be on a straight line. When the number of stations was five, the station linear configuration led to a narrow and long intersection region, resulting in a large deviation distance. The more dispersed distribution of stations leads to the minimum deviation distance. The percentage of deviation distance less than specific location accuracy (LA) can provide references on network design. 7 stations are sufficient to locate the lightning near and inside the network. 8–9 stations are necessary for an LA of 1 km when the lightning is 200 to 300 km away from the center of the network. The network is not suitable for locating the lightning from each station more than 400 km on average.


  • Open Access

    Location Accuracy Improvement of Long-range Lightning Detection Network In China by Compensating Ground Wave Propagation Delay

    by Junchao Zhang, Jiahao Zhou, Jie Li, Jiaying Gu, Qilin Zhang, Bingzhe Dai, Yao Wang, Jialei Wang, Yuqing Zhong, Qingda Li

    Very low frequency (VLF) electromagnetic waves distort along the long propagation path, and that causes the arrival time of the signals measured by the long-range lightning system to be delayed. In this paper, based on the propagation correction method by compensating the peak time delay of the ground wave, the location accuracy of the long-range lightning detection network in China is greatly improved. The improvement of the relative location accuracy and location offsets are evaluated by comparing with the Advanced Direction Time Lightning Detection System (ADTD) datasets. It shows that the mean relative accuracy is improved from 7.74 km to 4.32 km, and the median relative accuracy is improved from 7.28 km to 2.46 km. The mean westwards offset of the total lightning location data drops from 2.05 km to 0.93 km, and the mean southwards offset drops from 1.19 km to 0.63 km. In addition, it is found that the location accuracy will be greatly improved if the observation site affected by the terrain is removed. The mean relative location accuracy is further improved to 4.11 km and the median to 2.32 km.


  • Open Access

    Application of a Modified Empirical Wavelet Transform Method in VLF/LF Lightning Electric Field Signals

    by Bingzhe Dai, Jie Li, Jiahao Zhou, Yingting Zeng, Wenhao Hou, Junchao Zhang, Yao Wang, Qilin Zhang

    In this paper, to realize a better adaptive method for the lightning electric field signal denoising, we firstly compared the decomposition results of three methods called the EMD (empirical mode decomposition), the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), and the EWT (empirical wavelet transform) by artificial signals, respectively, and found that the EWT was better than the other two methods. Then, a MEWT (modified empirical wavelet transform) method based on the EWT was presented for processing the natural lightning signals data. By using our MEWT method, we processed three types of electric field signal data with different frequency bands radiated by the lightning step leader, the cloud pulse and the return stroke, respectively, and the VLF (very low frequency) lightning signals propagating different distances from 500 km to 3500 km, by using the data of the fast electric field change sensors from Nanjing Lightning Location Network (NLLN) in 2018 and the data of the fast electric field change sensors and the VLF electric antennas from the NUIST Wide-range Lightning Location System (NWLLS) in 2021. The results showed that our presented MEWT method could adaptively process different lightning signal data with different frequencies from the step leader, the cloud pulse, and the return stroke; for the lightning VLF signal data from 500 km to 3500 km, the MEWT also achieved a better noise reduction effect. After denoising the signal by using our MEWT, the detection ability of the fast electric field change sensor was improved, and more weak lightning signals could be identified.


  • Open Access

    Preliminary Application of Long-Range Lightning Location Network with Equivalent Propagation Velocity in China

    by Jie Li, Bingzhe Dai, Jiahao Zhou, Junchao Zhang, Qilin Zhang, Jing Yang, Yao Wang, Jiaying Gu, Wenhao Hou, Bin Zou, et al.

    The equivalent propagation method adopts a variable propagation velocity in lightning location, minimizing the location error caused by various factors in the long-range lightning location network. To verify the feasibility of this method, we establish a long-range lightning location network in China. A new method is used to extract the ground wave peak points of the lightning sferics and is combined with the equivalent propagation velocity method for lightning location. By comparing with the lightning data detected by the lightning locating system called advanced direction and time-of-arrival detecting (ADTD) that has been widely used for tens of years in China, the feasibility of this method is initially verified. Additionally, it is found that the relative detection efficiency of our long-range lightning location network can reach 53%, the average location error is 9.17 km, and the detection range can reach more than 3000 km. The equivalent propagation method can improve the average location accuracy by ~1.16 km, compared with the assumed light speed of lightning-radiated sferic from the lightning stroke point to the observation station. The 50th percentile of the equal propagation velocity is 0.998c, which may be used in the long-range lightning location networks.