Title

A Prediction on the Tropical Cyclone Intensification Using Logistical Model on Its Size

Student Classification

Senior

Faculty Mentor

Dr. Yuh-Lang Lin

Department

Applied Mathematics

Document Type

Poster

Publication Date

Spring 2019

Abstract

The prediction and forecasting of tropical cyclones (TC) in the United States has evolved significantly due to advancing technology over the years. However, according to the National Hurricane Center there has been no progress in predicting a cyclones change in intensity. Rapid intensification (RI) has been difficult to predict due to lack of knowledge on what physical processes controls these events. Rapid intensification is when a tropical cyclone's maximum sustained winds increase dramatically over a short period of time. In this study, a rapid intensification period is considered to be anything greater than or equal to 30kt over 24 hours. Previous study has discovered that there are some correlations between RI and the size of TC. Three parameters are used to classify the size of a tropical cyclone: radius of maximum wind (RMW), radius of outermost closed isobar (ROCI), and the average 34-kt radius. In this study we will use statistical models to predict the chance of RI from the size of the cyclone. We want to figure out if there is any correlation between the size of a cyclone and RI. We also want to quantify and classify storms by size and figure out the significance effect of the TC size over the RI. Lastly we create a statistical model to predict the probability of RI based on the TC size. The data for this study comes from the extended-best track (EBT) dataset. The data is separated into 24-h intervals between RI periods and Non-RI periods. Our techniques will be correlation analysis, logistic modeling, and regression models. Results shall indicate that there is correlation between storms that go under RI and its size. Smaller storms are more likely to undergo RI. There is also negative correlation between RMW and the average 34-kt radius with the change in intensity. However there is no correlation between ROCI and change in intensity. Our model will predict the significant variables that effect RI are RMW and the average 34-kt radius. Our investigation is to identify if size plays a role in a the rapid intensification of a tropical cyclone. This information is valuable for linking smaller storms to RI, thus will be useful for prediction of RI by using the TC size.

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