Publication Date

5-2017

Date of Final Oral Examination (Defense)

3-3-2017

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Mathematics

Department

Mathematics

Supervisory Committee Chair

Jaechoul Lee, Ph.D.

Supervisory Committee Member

Partha S. Mukherjee, Ph.D.

Supervisory Committee Member

Jodi L. Mead, Ph.D.

Abstract

A major winter storm brought up to 42 inches of snow in parts of the Mid-Atlantic and Northeast United States for January 22-24, 2016. The blizzard of January 2016 impacted about 102.8 million people, where at least 55 people died due to the snowstorm and it caused economic losses in a range of $500 million to $3 billion. This thesis studies two important aspects of extreme snow events: maximum snowfall and maximum snow depth. We apply extreme value methods to extreme snowfall and snow depth data from the New York City area to examine if there are any significant linear trends in extreme snow events and understand how likely the winter storm was in terms of return levels. We find that 87.5-th percentile snowfall and 75-th percentile snow depth have increased by 0.564 inches and 0.559 inches decade-1, respectively, whereas the annual maximum snowfall and snow depth series show insignificant increases. Our analysis shows that the 2016 blizzard was indeed an extreme snow event equivalent to about a 40-year return level in the New York City area. Our methods are thoroughly illustrated with details and expressions for practitioners wishing to use extreme value methods in applications.

DOI

https://doi.org/10.18122/B2TT4V

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