Automated lightning flash detection in nighttime visible satellite data

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Abstract

The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) nighttime visible channel was designed to detect earth-atmosphere features under conditions of low illumination (e.g., near the solar terminator or via moonlight reflection). However, this sensor also detects visible light emissions from various terrestrial sources (both natural and anthropogenic), including lightning-illuminated thunderstorm tops. This research presents an automated technique for objectively identifying and enhancing the bright steaks associated with lightning flashes, even in the presence of lunar illumination, derived from OLS imagery. A line-directional filter is applied to the data in order to identify lightning strike features and an associated false color imagery product enhances this information while minimizing false alarms. Comparisons of this satellite product to U.S. National Lightning Detection Network (NLDN) data in one case as well as to a lightning mapping array (LMA) in another case demonstrate general consistency to within the expected limits of detection. This algorithm is potentially useful in either finding or confirming electrically active storms anywhere on the globe, particularly those occurring in remote areas where surface-based observations are not available. Additionally, the OLS nighttime visible sensor provides heritage data for examining the potential usefulness of the Visible-Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) on future satellites including the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). The VIIRS DNB will offer several improvements to the legacy OLS nighttime visible channel, including full calibration and collocation with 21 narrowband spectral channels. © 2011 American Meteorological Society.

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Bankert, R. L., Solbrig, J. E., Lee, T. F., & Miller Cira, S. D. (2011). Automated lightning flash detection in nighttime visible satellite data. Weather and Forecasting, 26(3), 399–408. https://doi.org/10.1175/WAF-D-10-05002.1

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