Date of Award
Spring 5-2016
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Applied Physics
Department
Physics
Major Professor
Dr. Juliette Ioup
Second Advisor
Dr. Stanley Chin-Bing
Third Advisor
Gustave Michel
Abstract
Sea state is a subjective quantity whose accuracy depends on an observer’s ability to translate local wind waves into numerical scales. It provides an analytical tool for estimating the impact of the sea on data quality and operational safety. Tasks dependent on the characteristics of local sea surface conditions often require accurate and immediate assessment. An attempt to automate sea state classification using eleven years of ship motion and sea state observation data is made using parametric modeling of distribution-based confidence and tolerance intervals and a probabilistic model using sea state frequencies. Models utilizing distribution intervals are not able to exactly convert ship motion data into various sea states scales with significant accuracy. Model averages compared to sea state tolerances do provide improved statistical accuracy but the results are limited to trend assessment. The probabilistic model provides better prediction potential than interval-based models, but is spatially and temporally dependent.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Teichman, Jason A., "Automated Sea State Classification from Parameterization of Survey Observations and Wave-Generated Displacement Data" (2016). University of New Orleans Theses and Dissertations. 2199.
https://scholarworks.uno.edu/td/2199
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.