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Automated tropical cyclone eye detection using discriminant analysis

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dc.contributor.authorDeMaria, Robert, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorDraper, Bruce, committee member
dc.contributor.authorSchubert, Wayne, committee member
dc.date.accessioned2016-01-11T15:14:03Z
dc.date.available2016-01-11T15:14:03Z
dc.date.issued2015
dc.description.abstractEye formation is often associated with rapid intensification of tropical cyclones, so this information is very valuable to hurricane forecasters. Linear and Quadratic Discriminant Analysis (LDA and QDA) were utilized to develop a method for objectively determining whether or not a tropical cyclone has an eye. The input to the algorithms included basic storm information that is routinely available to forecasters, including the maximum wind, latitude and longitude of the storm center, and the storm motion vector. Infrared imagery from geostationary satellites in a 320 km by 320 km region around each storm was also used as input. Principal Component Analysis was used to reduce the dimension of the IR dataset. The ground truth for the algorithm development was the subjective determination of whether or not a tropical cyclone had an eye made by hurricane forecasters. The input sample included 4109 cases at 6 hr intervals for Atlantic tropical cyclones from 1995 to 2013. Results showed that the LDA and QDA algorithms successfully classified about 90% of the test cases. The best algorithm used a combination of basic storm information and principal components from the IR imagery. These included the maximum winds, the storm latitude and components of the storm motion vector, and 10 PCs from eigenvectors that primarily represented the symmetric structures in the IR imagery. The QDA version performed a little better using a Peirce Skill Score, which measures the ability to correctly classify cases. The LDA and QDA algorithms also provide the probability that each case contains an eye. The LDA version performed a little better using the Brier Skill Score, which measures the utility of the class probabilities. The high success rate indicates that the algorithm can reliably reproduce what forecasters are currently doing subjectively. This algorithm would have a number of applications, including providing forecasters with an objective way to determine if a tropical cyclone has or is becoming more likely to form an eye. The probability information and its time trends could be used as input to other algorithms, such as existing operational forecast methods for estimating tropical cyclone intensity changes.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDeMaria_colostate_0053N_13387.pdf
dc.identifier.urihttp://hdl.handle.net/10217/170410
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjecteye detection
dc.subjectlinear discriminant analysis
dc.subjectprincipal component analysis
dc.subjectquadratic discriminant analysis
dc.subjecttropical cyclone
dc.titleAutomated tropical cyclone eye detection using discriminant analysis
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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