Climatological analyses of LMA data with an open‐source lightning flash‐clustering algorithm. Issue 14 (29th July 2016)

• Record Type: Journal Article
• Authors: Fuchs, Brody R.; Bruning, Eric C.; Rutledge, Steven A.; Carey, Lawrence D.; Krehbiel, Paul R.; Rison, William
• Is Part Of: Journal of geophysical research. Volume 121:Issue 14(2016)
• Page Start: 8625
• Page End: 8648
• Publication Date: 2016-07-29
• Subjects: lightning -- lightning detection -- convective processes -- cloud physics; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD024663
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Approximately 63 million lightning flashes have been identified and analyzed from multiple years of Washington, D. C., northern Alabama, and northeast Colorado lightning mapping array (LMA) data using an open‐source flash‐clustering algorithm. LMA networks detect radiation produced by lightning breakdown processes, allowing for high‐resolution mapping of lightning flashes. Similar to other existing clustering algorithms, the algorithm described herein groups lightning‐produced radiation sources by space and time to estimate total flash counts and information about each detected flash. Various flash characteristics and their sensitivity to detection efficiency are investigated to elucidate biases in the algorithm, detail detection efficiencies of various LMAs, and guide future improvements. Furthermore, flash density values in each region are compared to corresponding satellite estimates. While total flash density values produced by the algorithm in Washington, D. C. (~20 flashes km −2 yr −1 ), and Alabama (~35 flashes km −2 yr −1 ) are within 50% of satellite estimates, LMA‐based estimates are approximately a factor of 3 larger (50 flashes km −2 yr −1 ) than satellite estimates in northeast Colorado. Accordingly, estimates of the ratio of in‐cloud to cloud‐to‐ground flashes near the LMA network (~20) are approximately a factor of 3 larger than satellite estimates in Colorado. These large differences between estimates may be related to the distinct environment conducive to intense convection, low‐altitude flashes, and unique charge structures in northeast Colorado. Key Points: LMA flash‐clustering algorithm able to process years of data and provide insight into algorithm performance and LMA detection LMA flash density differences with satellite values largest in Colorado and may be dependent on flash altitude and environment Variability in flash characteristics highlights performance of algorithm and guides future improvements