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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-5529 |
| Date | 17 May 2014 |
| Creators | Grimes, Alexandria Danielle |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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