Date of Award

3-21-2019

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Omar A. Nava, PhD

Abstract

In the absence of wind speed data from aircraft reconnaissance of tropical cyclones (TCs), analysts rely on remote sensing tools to estimate TC intensity. For over 40 years, the Dvorak technique has been applied to estimate intensity using visible and infrared (IR) satellite imagery, but its accuracy is sometimes limited when the radiative effects of high clouds obscure the TC convective structure below. Microwave imagery highlights areas of precipitation and deep convection revealing different patterns than visible and IR imagery. This study explores application of machine learning algorithms to identify patterns in microwave imagery to infer storm intensity, particularly focusing on weaker storms where other analysis methods struggle. An analysis of 91 GHz Special Sensor Microwave Imager/Sensor imagery onboard various Defense Meteorological Satellite Program assets from February 2006 to 2017 is presented. Incorporating pattern recognition methods into the current analysis process at the Joint Typhoon Warning Center has the potential to significantly improve TC intensity estimates across all basins of responsibility.

AFIT Designator

AFIT-ENP-MS-19-M-087

DTIC Accession Number

AD1078205

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