Advisor

Brown, Michael E.

Committee Member

Mercer, Andrew.

Committee Member

Wax, Charles.

Date of Degree

1-1-2012

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

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