Automated Identification of Initial Storm Electrification and End-of-Storm Electrification Using Electric Field Mill Sensors

[摘要] Kennedy Space Center (KSC) operations are located in a region which experiences one of the highest lightning densities across the United States. As a result, on average, KSC loses almost 30 minutes of operational availability each day for lightning sensitive activities. KSC is investigating using existing instrumentation and automated algorithms to improve the timeliness and accuracy of lightning warnings. Additionally, the automation routines will be warning on a grid to minimize under-warnings associated with not being located in the center of the warning area and over-warnings associated with encompassing too large an area. This study discusses utilization of electric field mill data to provide improved warning times. Specifically, this paper will demonstrate improved performance of an enveloping algorithm of the electric field mill data as compared with the electric field zero crossing to identify initial storm electrification. End-of-Storm-Oscillation (EOSO) identification algorithms will also be analyzed to identify performance improvement, if any, when compared with 30 minutes after the last lightning flash.

[发布日期] 2017-01-26

[学科分类] 大气科学