Analysis of Environmental Factors Contributing to the Eyewall Replacement Cycle of Hurricanes
Open Access
- Author:
- Christino, Martha
- Area of Honors:
- Meteorology
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Paul Markowski, Thesis Supervisor
Paul Markowski, Thesis Honors Advisor
Anthony Carl Didlake, Jr., Faculty Reader
George Spencer Young, Thesis Supervisor - Keywords:
- Hurricanes
Machine Learning
Eyewall Replacement Cycle
Hurricane Intensification
Environmental - Abstract:
- Even the most advanced hurricane forecast models have difficulty predicting eyewall replacement cycles (ERCs) in tropical cyclones. Most research has attempted to solve this problem by working to understand the dynamic and kinematic drivers of an ERC. This project proposes an alternative approach focused on analyzing the changes in measurable environmental factors and utilizing a machine learning algorithm to predict the ERC. The aim of the first phase of this project is to establish which environmental factors are linked to the initial development of a secondary eye wall. Thirty-seven occurrences of secondary eyewall formation (SEF) in hurricanes between 1984 and 2018 were selected based on the criteria used in Sitkowski, et al. (2011). Each SEF event was matched with a similarly intensifying hurricane that did not experience a subsequent SEF event based on the year and storm intensity. Using environmental data from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictor files, the change in each environmental variable at six-hour intervals for twenty-four hours before the start of SEF was analyzed. The environmental variables that experience the most significant change prior to SEF will determine which variables should be used as predictors in a machine learning program designed to predict SEF onset. The goal of this research is to create an algorithm capable of predicting a SEF event twenty-four hours in advance. This algorithm will be compared to existing statistical SEF prediction schemes. Predicting ERC events will allow hurricane track and intensity models to produce more accurate forecasts and emergency response centers to accordingly alter evacuation zones, resulting in decreased economic loss and fatalities.