Abstracts Category : Other

Add abstract

Want to add your dissertation abstract to this database? It only takes a minute!

Search abstract

Search for abstracts by subject, author or institution

Share this abstract

dissertation.com
on Facebook

Severe weather parameters and their effectiveness on forecasting tropical cyclone induced tornadoes

by Jonathan Weaver

Institution:	Mississippi State University
Year:	2017
Keywords:	statistics; artificial intelligence; tropics; weather; meteorology; forecasting; severe weather; tornado; hurricane; tropical cyclone
Posted:	02/01/2018
Record ID:	2154423
Full text PDF:	http://sun.library.msstate.edu/ETD-db/theses/available/etd-03222017-155929/;

Abstract

Tropical 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. Advisors/Committee Members: Andrew E. Mercer (chair), Jamie L. Dyer (committee member), Kimberly M. Wood (committee member).