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High-dimensional manifold geostatistics

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TitleInfo
Title
High-dimensional manifold geostatistics
Name (type = personal)
NamePart (type = family)
Dalal
NamePart (type = given)
Chintan
NamePart (type = date)
1981-
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Chintan Dalal
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author
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Metaxas
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Dimitris N
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Dimitris N Metaxas
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Advisory Committee
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chair
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Pavlovic
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Vladimir
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Vladimir Pavlovic
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Advisory Committee
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internal member
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Bekris
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Kostas
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Kostas Bekris
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Advisory Committee
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Stroud
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Jonathan
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Jonathan Stroud
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Advisory Committee
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outside member
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Nychka
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Douglas
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Douglas Nychka
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Advisory Committee
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Rutgers University
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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DateCreated (qualifier = exact)
2019
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2019-01
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2019
Place
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xx
Language
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eng
Abstract (type = abstract)
An understanding of the Earth's climate system benefits all sectors of the economy and environment. Several challenges faced when modeling the Earth's climate system include: estimating geographical features of global datasets, making inferences from multiple data-products, and providing diagnostic tools for complex Earth models. Existing geostatistical approaches address these challenges by modeling points on a high-dimensional space. However, we know that many of the climate datasets additionally have inherent high-dimensional geometric structures. In this dissertation, I provide new insights into problems in climate data science by exploring high-dimensional geometric structures on a manifold. First, I will discuss an approach to improve future projections of a climate variable (e.g., sea-level changes) by learning the scale of correlation, an essential regional feature of climate datasets. Second, I will provide a new framework for data-fusion from multiple sources of information for a given climate variable. Third, I will describe diagnostic tools we created to compare and emulate various Earth system models from numerous international teams and for differing future climate scenarios (e.g. precipitation changes in 2090). With these contributions, I demonstrate that we can improve the inferences made from geostatistical models by including information about the high-dimensional structures of climate datasets. The proposed novel framework will benefit not only the climate community but also decision makers when identifying plans to mitigate the impact of climate change.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Geology -- Statistical methods
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_9407
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (144 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Chintan Dalal
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-gbet-rb23
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Dalal
GivenName
Chintan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-12-06 19:29:05
AssociatedEntity
Name
Chintan Dalal
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
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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)
2019-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-08-02
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2018-12-07T14:53:01
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2018-12-07T14:53:01
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