Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Mercer, Andrew E.
Committee Member
Dyer, Jamie L.
Committee Member
Wood, Kimberly M.
Date of Degree
5-6-2017
Document Type
Graduate Thesis - Open Access
Major
Professional Meteorology
Degree Name
Master of Science
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
ropical cyclone-induced tornadoes (TCIT) exacerbate the devastation that landfalling tropical cyclones have on the United States. This research applied machine learning techniques in conjunction with midlatitude severe weather parameters to create an artificial intelligence (AI) capable of predicting TCIT occurrence. Severe weather diagnostic variables were collected at thousands of gridpoints from the North American Regional Reanalysis (NARR) to characterize the environments within tropical cyclones between 1991 and 2011. A support vector machine (SVM) was generated in various configurations to obtain the most effective AI. This approach revealed many parameters that were ineffective at predicting TCITs (primarily those utilizing the effective inflow layer). In addition, the most highly configured AI were capable of predicting TCIT occurrence with a Heidke Skill Score around 0.48.
URI
https://hdl.handle.net/11668/17558
Recommended Citation
Weaver, Jonathan Curtis, "Severe Weather Parameters and their Effectiveness on Forecasting Tropical Cyclone Induced Tornadoes" (2017). Theses and Dissertations. 3930.
https://scholarsjunction.msstate.edu/td/3930
Comments
statistics||artificial intelligence||tropics||weather||meteorology||forecasting||severe weather||tornado||hurricane||tropical cyclone