As projections indicate, the U.S. National Airspace System (NAS) vulnerability to lightning strikes is growing due to increased severity and frequency of convective storms. Lightning hazards pose risks to personnel safety and may cause power outages, and therefore they can impact reliability and performance of the airport’s facilities if they are not appropriately fortified. Although lightning-flash data with high temporal and spatial accuracy is available for the continental U.S., measuring airport facility lightning-induced outages remains challenging. Studies have shown that lightning-induced outages have been under-represented in the Federal Aviation Administration’s (FAA) official outage classification data [1], [2]. In our study we try to understand the relationship between lighting strikes and airport facility outages, and to determine the sensitivity thresholds of lightning hazards from a spatial and temporal perspective. We use data from the National Lightning Detection Network (NLDN) as well as the FAA facility outages. Although the Baltimore-Washington International (BWI) airport is used as a case study, our proposed method can easily be expanded to other airports in the NAS. By applying a spatial-temporal distribution analysis of lightning strikes and facility outages from 2009 to 2017, we propose a new classification of lightning-induced outages using machine learning (ML) algorithms. Our results show that approximately 20% of BWI outages have been misclassified, thus corroborating the tendency of underestimation of lighting impact to the NAS. Furthermore, we identify lightning hazard thresholds that help estimate the sensitivity of the airport’s facilities to lightning-strike peak intensity and multiplicity. The study findings can be used to assist facility managers in creating better mitigation strategies through adequate lightning protection, grounding, bonding, and shielding (LPGBS), while maintaining reliable and safe operations in the NAS.
Characterizing Lightning-Strike Hazard to Airport Facilities: A Case Study of Baltimore Washington International Airport
2019-04-01
3288838 byte
Conference paper
Electronic Resource
English