Theses and Dissertations

Issuing Body

Mississippi State University


Mercer, Andrew E.

Committee Member

Dyer, Jamie L.

Committee Member

Brown, Michael E.

Date of Degree


Document Type

Graduate Thesis - Open Access


Geosciences (Professional Meteorology)

Degree Name

Master of Science


College of Arts and Sciences


Department of Geosciences


Recent improvements in numerical weather model resolution open the possibility of producing forecasts for lightning using indirect lightning threat indicators well in advance of an event. This research examines the feasibility of a statistical machine-learning algorithm known as a support vector machine (SVM) to provide a probabilistic lightning forecast for Mississippi at 9 km resolution up to one day in advance of a thunderstorm event. Although the results indicate that SVM forecasts are not consistently accurate with single-day lightning forecasts, the SVM performs skillfully on a data set consisting of many forecast days. It is plausible that errors by the numerical forecast model are responsible for the poorer performance of the SVM with individual forecasts. More research needs to be conducted into the possibility of using SVM for lightning prediction with input data sets from a variety of numerical weather models.