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Storm surge-producing extratropical cyclones in the northeastern United States in observations and models

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TitleInfo
Title
Storm surge-producing extratropical cyclones in the northeastern United States in observations and models
Name (type = personal)
NamePart (type = family)
Catalano
NamePart (type = given)
Arielle J.
NamePart (type = date)
1990-
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Arielle J. Catalano
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Broccoli
NamePart (type = given)
Anthony J
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Anthony J Broccoli
Affiliation
Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Lintner
NamePart (type = given)
Benjamin R
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Benjamin R Lintner
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Decker
NamePart (type = given)
Steven G
DisplayForm
Steven G Decker
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Kapnick
NamePart (type = given)
Sarah B
DisplayForm
Sarah B Kapnick
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateOther (qualifier = exact); (type = degree)
2018-10
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2018
Place
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xx
DateCreated (encoding = w3cdtf)
2018
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In the northeastern United States, extratropical cyclones (ETCs) are associated with the majority of the largest storm surges, which significantly impact coastal regions. We characterize the synoptic evolution of the largest ETC-driven surge events in observations and a long record from a coupled climate model representing recent climate conditions. A k-means cluster analysis is applied to the top 100 observed surge-producing ETCs at select locations (Sewells Point, Virginia; The Battery, New York; and Boston, Massachusetts) to group similar circulation features. These distinct patterns suggest that the largest surges are generated when slowly propagating ETCs encounter a strong anticyclone, which produces a tighter pressure gradient and longer duration of onshore winds. Multiple clusters feature a slower-than-average storm and a strong anticyclone, indicating that various circulation scenarios with these features can produce a large surge. This favorable environment is influenced by El NiƱo conditions, and maximum surge occurs preferentially during the positive phase of PNA and the negative phases of AO/NAO.

Return periods of the largest ETC-driven surge events are difficult to estimate owing to the short duration of high-quality observational datasets, so a long simulation from a coupled model, GFDL FLOR, is employed. Distributions of meteorological quantities that influence surge height (i.e. central pressure and surface winds) indicate that the longer integration contains a greater number of extreme ETCs. An exceedance probability risk assessment of the strongest impacts demonstrates a consistent underestimation in historical-length records compared to return levels estimated from the full FLOR simulation. This indicates that if the underlying distributions of observed metrics are similar to those of the 1505-year record, the actual frequency of extreme events is being underestimated.

Comparisons of cyclone statistics between FLOR and a reanalysis product, CFSR, exhibit biases in quantitative measures of storm surge and intensity, but characteristics of the distributions of these quantities are representative of features of a climate constrained by observations. A k-means cluster analysis of the synoptic evolution of storm surge events estimated using a regression-based index displays similar circulation features to clusters of observed events. At The Battery and Sewells Point, clusters containing the majority of the largest estimated storm surges exhibit a strong anticyclone and a slow-moving cyclone. Discrepancies at Boston are related to approximations made by the regression index applied to identify and arrange clustered meteorological patterns.
Subject (authority = RUETD)
Topic
Atmospheric Science
Subject (authority = LCSH)
Topic
Cyclones--United States
Subject (authority = LCSH)
Topic
Storm surges--United States
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Identifier
ETD_9074
Identifier (type = doi)
doi:10.7282/T3G44TXC
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 85 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Arielle J. Catalano
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Catalano
GivenName
Arielle J.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-06-21 12:04:51
AssociatedEntity
Name
Arielle J. Catalano
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-10-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2018-06-21T15:54:42
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2018-06-21T15:54:42
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