Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features

Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m.

Leader-chasing behavior in negative artificial triggered lightning flashes

Two special cases of dart leader propagation were observed by the high-speed camera in the leader/return stroke sequences of a classical triggered lightning flash and an altitude-triggered lightning flash, respectively. Different from most of the subsequent return strokes preceded by only one leader, the return stroke in each case was preceded by two leaders occurring successively and competing in the same channel, which herein is named leader-chasing behavior. In one case, the polarity of the latter leader was opposite to that of the former leader and these two combined together to form a new leader, which shared the same polarity with the former leader. In the other case, the latter leader shared the same polarity with the former leader and disappeared after catching up with the former leader. The propagation of the former leader in this case seems not to be significantly influenced by the existence of the latter leader.

A performance analysis of the North American Lightning Detection Network using CN Tower lightning data

The North American Lightning Detection Network (NALDN) is a commercial lightning detection network operated by Vaisala Inc., and is composed of the U.S. National Lightning Detection Network (NLDN) and the Environment Canada owned Canadian Lightning Detection Network (CLDN). The CN Tower is one of the best sites in the world to observe the lightning phenomenon and provides an excellent opportunity to evaluate the performance of the NALDN in the Toronto area. Using CN Tower lightning data acquired during 2005, the performance characteristics of the NALDN were thoroughly evaluated, including the flash detection efficiency (DE), stroke DE, absolute location error, peak current estimation and location accuracy model (50%, 90% and 99% error ellipses) error. Although a similar test was performed using rocket-triggered lightning in Florida at Camp Blanding, this test evaluated a completely different region of the NALDN. Moreover, rocket-triggered lightning artificially initiates a lightning discharge, whereas lightning events to the CN Tower occur naturally and are similar to discharges that occur to tall structures or objects at high altitude or mountainous areas. Excluding two flashes understood to be composed of M-components, the NALDN detected 7 out of 7 flashes recorded at the CN Tower, resulting in a 100% flash DE. Furthermore, the NALDN detected 22 out of 39 strokes recorded at the CN Tower, resulting in a stroke DE of 56%. Relative to the CN Tower, the NALDN was found to have a median absolute location error of 0.356 km and a mean error of 0.390 km for the 22 strokes it detected. It was also demonstrated that the NALDN stroke location error seems to have a large bias towards the north of the CN Tower and a slight bias towards the east, with 19 of the 22 strokes predicted north-east of the CN Tower. The 50%, 90% and 99% error ellipses provided by the NALDN were also evaluated. It was found that 73% (16 out of the 22) detected strokes were enclosed by the 50% error ellipse, 91% (20 out of the 22) detected strokes were enclosed by the 90% error ellipse and 95% (21 out of the 22) detected strokes were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axes is set at 0.4 km by Vaisala, and 21 out of the 22 detected strokes had a semi-major axis length of 0.4 km, suggesting that the median location error for CN Tower strokes is 0.4 or less. The 0.356 km median location error obtained for the 22 detected strokes appears to support this.

A performance analysis of the North American Lightning Detection Network using CN Tower lightning data

The North American Lightning Detection Network (NALDN) is a commercial lightning detection network operated by Vaisala Inc., and is composed of the U.S. National Lightning Detection Network (NLDN) and the Environment Canada owned Canadian Lightning Detection Network (CLDN). The CN Tower is one of the best sites in the world to observe the lightning phenomenon and provides an excellent opportunity to evaluate the performance of the NALDN in the Toronto area. Using CN Tower lightning data acquired during 2005, the performance characteristics of the NALDN were thoroughly evaluated, including the flash detection efficiency (DE), stroke DE, absolute location error, peak current estimation and location accuracy model (50%, 90% and 99% error ellipses) error. Although a similar test was performed using rocket-triggered lightning in Florida at Camp Blanding, this test evaluated a completely different region of the NALDN. Moreover, rocket-triggered lightning artificially initiates a lightning discharge, whereas lightning events to the CN Tower occur naturally and are similar to discharges that occur to tall structures or objects at high altitude or mountainous areas. Excluding two flashes understood to be composed of M-components, the NALDN detected 7 out of 7 flashes recorded at the CN Tower, resulting in a 100% flash DE. Furthermore, the NALDN detected 22 out of 39 strokes recorded at the CN Tower, resulting in a stroke DE of 56%. Relative to the CN Tower, the NALDN was found to have a median absolute location error of 0.356 km and a mean error of 0.390 km for the 22 strokes it detected. It was also demonstrated that the NALDN stroke location error seems to have a large bias towards the north of the CN Tower and a slight bias towards the east, with 19 of the 22 strokes predicted north-east of the CN Tower. The 50%, 90% and 99% error ellipses provided by the NALDN were also evaluated. It was found that 73% (16 out of the 22) detected strokes were enclosed by the 50% error ellipse, 91% (20 out of the 22) detected strokes were enclosed by the 90% error ellipse and 95% (21 out of the 22) detected strokes were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axes is set at 0.4 km by Vaisala, and 21 out of the 22 detected strokes had a semi-major axis length of 0.4 km, suggesting that the median location error for CN Tower strokes is 0.4 or less. The 0.356 km median location error obtained for the 22 detected strokes appears to support this.