Term of Award
Spring 2021
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Daniel Jones
Committee Member 1
Yan Wu
Committee Member 2
Yongki Lee
Abstract
The vertical distribution of ozone in the atmosphere has significant consequences for climate and life's existence on the Earth's surface. Methods of defining the vertical ozone structure rely on the combination of physical, chemical, and dynamic properties. As a step toward an alternative approach for describing vertical structure in ozone concentration, here we apply an unsupervised classification technique (i.e., Gaussian mixture modeling or GMM) to observed and modeled ozone profiles. As GMM is automated, standardized, and robust in nature, it automatically identifies spatially coherent classes without using any latitude or longitude information. Possibly it can be a useful complement to existing classification to deal with an enormous, ever-increasing volume of observational and computer model data.
OCLC Number
1382430848
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/31l71c/alma9916564849902950
Recommended Citation
Fahrin, Fouzia, "Unsupervised Classification of Atmospheric Ozone Profiles" (2021). Electronic Theses and Dissertations. 2209.
https://digitalcommons.georgiasouthern.edu/etd/2209
Research Data and Supplementary Material
No
