A point process analysis of cloud-to-ground lightning strikes in urban and rural Oklahoma areas
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ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-10-10Online Publication Date
2018-10-10Print Publication Date
2019-02Permanent link to this record
http://hdl.handle.net/10754/630034
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Lightning is a natural event that can cause severe human and financial losses. This work introduces a probability risk assessment of the occurrence of the cloud-to-ground (CG) lightning in urban and rural areas of Oklahoma. CG lightning, although not the most common type, is the most damaging. Previous studies have reported that urban areas experience an increase in the frequency of CG lightning events, during warm months. This increase poses serious threats to urban industries and electronic systems. Lightning strikes are point process in nature, although this quality has not been exploited in previous studies. We utilize a probability model for the spatiotemporal point process of CG lightning to estimate the risk of a CG lightning strike for a particular location and time. The data are discretized into small spatiotemporal cells (voxels), and then, we fit a generalized additive model with a complementary log–log link function using the location and the day of occurrence of the strike as explanatories. On the basis of this model, we compared the urban and rural monthly fitted rates of CG lightning strikes. We found that the rate in the rural area is smaller than the rate in the Tulsa metropolitan area during the warm months; however, it is larger than the rate in the Oklahoma metropolitan area during May and June.Citation
Hernandez-Magallanes I, Genton MG (2018) A point process analysis of cloud-to-ground lightning strikes in urban and rural Oklahoma areas. Environmetrics: e2535. Available: http://dx.doi.org/10.1002/env.2535.Sponsors
The authors thank the Reviewers for the valuable comments provided; Professors Richard Orville, Shaima L. Nasiri, and Courtney Schumacher for their input in the preliminary stage of this work; and Ron Holle at Vaisala Inc. for providing the lightning data. The research described here was supported by King Abdullah University of Science and Technology.Publisher
WileyJournal
EnvironmetricsDOI
10.1002/env.2535Additional Links
https://onlinelibrary.wiley.com/doi/full/10.1002/env.2535ae974a485f413a2113503eed53cd6c53
10.1002/env.2535