Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

Abstract

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA)-which are important elements in the transition of the LJA concept from a research to an operational-based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid) and lightning information (flash rate density). Evaluations show that the spatial scale of tracked features or storm clusters has a large impact on the lightning jump system performance, where increasing spatial scale results in a decreased dynamic range of the system's performance. This framework also serves as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection and false alarm ratio statistics. Of the algorithm parameters tested, the sigma-level (i.e., a metric of lightning jump strength) and flash rate threshold influence the system's performance the most. Finally, minor changes in verification methods can dramatically impact the evaluation of the lightning jump system.

A Combined IR-GPS Satellite Analysis for Potential Applications in Detecting and Predicting Lightning Activity

“…Recently the term "lightning jump" has been coined to indicate the moment of development of a storm when the number of electric discharges increases in a very short time. This indicator is quite easy to extract both from the data provided by ground lightning networks [17] and, even better, from the data acquired by instruments embarked on a satellite such as the Geostationary Operational Environmental Satellite (GOES)-R geostationary lightning mapper (GLM) [18].…”

Polarimetric radar characteristics of storms with and without lightning activity

“…The next‐generation of geostationary satellites that includes the Geostationary Operational Environmental Satellite–R (GOES‐R) with Geostationary Lightning Mapper (GLM) sensor [ Goodman et al ., ] and the Meteosat Third Generation (MTG) with Lightning Imager (LI) sensor [ Stuhlmann et al ., ] will map total lightning activity continuously day and night with near‐uniform spatial resolution of 8 km, frame rate of 2 ms, and a product latency of less than 20 s for GLM. The refinement of our current understanding about the processes relating cloud microphysical signatures and lightning density could be very useful for several applications, such as data assimilation [e.g., Fierro et al ., , ; Mansell , ; Qie et al ., ], nowcasting [e.g., Goodman et al ., ; Schultz et al ., , , ], and rainfall estimation [e.g., Soula , ; Wang et al ., ; Xu et al ., ].…”

Tropical Oceanic Thunderstorms Near Kwajalein and the Roles of Evolution, Organization, and Forcing in Their Electrification

“…Clusters that are within 20 km of a previous cluster within 20 min are spliced into that cluster's time history, in order to mitigate “broken tracks,” wherein the algorithm restarts tracking a cluster with a new identification number. For demonstration of the WDSS‐II algorithm suite and examples of clustering, see Schultz et al, (, their Figure 2 ) and Lakshmanan and Smith (, their Figure 1).…”