Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

First Advisor

Homaifar, Dr. Abdollah


Accurate intensity estimation of tropical cyclones (TC) is an important topic of research due to its economic impact and public safety concerns. An accurate measure of the current wind strength is necessary to accurately predict TC intensity. We have developed and tested automated method to estimate TC intensity based on the existing historical satellite images alone. The Hurricane Satellite data (HURSAT–B1) is used to develop the algorithm, which focuses on the North Atlantic from 1978-2009. The algorithm is trained and validated using aircraft reconnaissance-based data. Here, the data is restricted to include only fixes that are over water and are south of 45˚N. This subset comprises of 2,016 measurements in 165 storms from 1988 through 2006. The proposed intensity estimation algorithm has two parts: temporal constraints and spatial (image) analysis. The temporal analysis uses the age of the cyclone, 6, 12 and 24 hour prior intensities as predictors of the expected intensity. The 10 closest analogs determined by a K-nearest-neighbor algorithm are averaged to obtain an estimate of the intensity of unknown TC. The resulting average mean absolute error is 4.8 kt (50% of estimates have MAE within 4.4 kt). The current analysis has the potential to decrease the DT noise and to provide new temporal constraints on DT. The image intensity estimation algorithm uses satellite images for intensity analysis. The expected intensity is estimated using the current image and earlier images from the 6, 12 and 24 hour before the current image as predictors. Several tests were implemented to statistically justify the proposed algorithm using the n-fold cross-validation where n is 165. The resulting average mean absolute error for the 165 storms is 10.9 kt (50% of points are within 10 kt) or 8.4 mb (50% of points are within 8 mb) and its accuracy is on par with other objective techniques. The proposed approach is an important tool for estimating the intensity of tropical cyclones due to its simplicity, objectivity and consistency.