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Extreme events are phenomena which occupy the tail-end of a distributions PDF. While atmospheric phenomena are decidedly non-Gaussian, the exact shape of these tails of a distribution are relatively unknown. From stochastic theory, it is noted that tails or extremes may be predicted by the behavior of power-law distribution. While prior research for the empirical search for power-laws has been heavily qualitative in nature, this study aims at the quantitative and statistical fitting and analysis of power-laws across the southeastern United States with respect to daily maximum and minimum temperatures. Utilizing a power-law fitting algorithm, we may fit power-law distributions to the PDFs of atmospheric maximum and minimum temperatures. After statistical analysis, we may note the universal significance of these power-law tails throughout the southeastern United States within regions of non-Gaussianity. Further, we analyze varying behavior of these significant power-laws within the distribution's PDF. From this, we may note and observe the behavior of these extremes events in relation to weather and climatic cycles.
A Thesis submitted to the Department of Earth, Ocean, and Atmospheric Science in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Philip Sura, Professor Directing Thesis; Zhaohua Wu, Committee Member; Mark Bourassa, Committee Member; Lydia Stefanova, Committee Member.
Florida State University
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