Advisor

Mercer, Andrew E.

Committee Member

Dyer, Jamie L.

Committee Member

Dixon, P. Grady

Date of Degree

1-1-2014

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

College of Arts and Sciences

Abstract

This research identifies large-scale synoptic controls that are relevant for rapid intensification (RI) in the Atlantic basin. Spatial statistical analysis techniques were performed on NASA MERRA data from 1979–2009. Rotated principal component analysis (RPCA) was performed, looking for common patterns in the datasets. The RPC’s were grouped using hierarchical clustering techniques, allowing for finding events similar in synoptic structure. The clustered events, representing the total RI and non-RI composites, were averaged yielding composite maps for different scenarios. To verify the results, a permutation test was done to show which variables are good distinguishers of RI and non-RI cases. These variables were used as input in two prediction schemes: logistic regression and support vector machine classification. The prediction scheme was a slight improvement in forecasting RI when using the synoptic variables mid-level vorticity, vertical velocity, low-level potential temperature and specific humidity, as the most significant in predicting RI.

URI

https://hdl.handle.net/11668/17905

Share

COinS