Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Brown, Michael E.
Committee Member
Mercer, Andrew.
Committee Member
Wax, Charles.
Date of Degree
5-12-2012
Document Type
Graduate Thesis - Open Access
Major
Geosciences
Degree Name
Master of Science
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
Ten years of lightning data was used to examine the lightning climatology in the Mid-South and to create a model capable of predicting severe hail storms using CG lightning. Cloud to ground lightning peaked reached a maximum in July and a minimum in January. Positive CG accounted for 5.3% of all strikes. The percentage of positive strikes reached a maximum in December and a minimum in August. Artificial intelligence along with logistic regression models were used for hail prediction. The 95% confidence intervals of the contingency statistics were used to determine the performance of the models. The linear cost 100 model and logistic regression had the highest performance and were tested with an independent data set. The logistic regression model outperformed the linear cost 100 model. The performance by both models was under the median statistics but within the 95% confidence interval.
URI
https://hdl.handle.net/11668/16593
Recommended Citation
Reagan, Matthew, "Using Cloud to Ground Lightning as a Forecast Tool for Severe Hail" (2012). Theses and Dissertations. 4888.
https://scholarsjunction.msstate.edu/td/4888