A Technique for Automated Detection of Lightning in Images and Video From the International Space Station for Scientific Understanding and Validation

A combination of Chiba University's METEOR camera, International Space Station Lightning Imaging Sensor (ISS LIS), Geostationary Lightning Mapper (GLM), National Lightning Detection Network (NLDN) data, and other space and ground based data sets were utilized to develop a METEOR-derived lightning identification technique to automatically identify lightning flashes within International Space Station video and still frame imagery. Approximately 14,000 frames were used from two METEOR camera videos. Zero lightning events were missed by the technique using manual inspection of both videos, and the technique did not identify other sources of light (e.g., city lights). Three-hundred and nine METEOR-identified flashes were matched with 289 GLM flashes and 285 ISS LIS flashes in the METEOR field of view on May 17, 2017. On average, the flash area determined by the analysis technique developed in this study was 266 km(2) smaller than the flash area observed by GLM. The primary reason for this difference in size was the spatial resolution of GLM and METEOR (>8 km vs. 260 m). When NLDN flashes were observed, there was a 200-500 km(2) increase in the algorithm-derived flash area within 100 ms of the NLDN time, indicative of return stroke processes as bright light is scattered through cloud top. Accurate reverse geolocation using lightning data alone was difficult due to the different spatial resolution, temporal resolution, and other geolocation assumptions between the camera images and comparison data. However, the use of satellite-derived city lights aided in the geolocation process for scientific comparisons. Plain Language Summary This work describes a technique developed to identify lightning features in still images and video frames from the International Space Station. This technique allows for utilization of the stunning imagery taken from station each day to study lightning properties from a new perspective for validation and scientific research. This paper walks through two different cases and highlights how the images provide additional scientific understanding on flash area and rapid changes in area related to other lightning datasets.