Persistent Link:
http://hdl.handle.net/10150/293533
Title:
Diagnostics and Generalizations for Parametric State Estimation
Author:
Nearing, Grey Stephen
Issue Date:
2013
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This dissertation is comprised of a collection of five distinct research projects which apply, evaluate and extend common methods for land surface data assimilation. The introduction of novel diagnostics and extensions of existing algorithms is motivated by an example, related to estimating agricultural productivity, of failed application of current methods. We subsequently develop methods, based on Shannon's theory of communication, to quantify the contributions from all possible factors to the residual uncertainty in state estimates after data assimilation, and to measure the amount of information contained in observations which is lost due to erroneous assumptions in the assimilation algorithm. Additionally, we discuss an appropriate interpretation of Shannon information which allows us to measure the amount of information contained in a model, and use this interpretation to measure the amount of information introduced during data assimilation-based system identification. Finally, we propose a generalization of the ensemble Kalman filter designed to alleviate one of the primary assumptions - that the observation function is linear.
Type:
text; Electronic Dissertation
Keywords:
Data Assimilation; Hidden Markov Models; Information Theory; Remote Sensing; Soil Moisture; Hydrology; Bayesian Analysis
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Hydrology
Degree Grantor:
University of Arizona
Advisor:
Gupta, Hoshin V.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleDiagnostics and Generalizations for Parametric State Estimationen_US
dc.creatorNearing, Grey Stephenen_US
dc.contributor.authorNearing, Grey Stephenen_US
dc.date.issued2013-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis dissertation is comprised of a collection of five distinct research projects which apply, evaluate and extend common methods for land surface data assimilation. The introduction of novel diagnostics and extensions of existing algorithms is motivated by an example, related to estimating agricultural productivity, of failed application of current methods. We subsequently develop methods, based on Shannon's theory of communication, to quantify the contributions from all possible factors to the residual uncertainty in state estimates after data assimilation, and to measure the amount of information contained in observations which is lost due to erroneous assumptions in the assimilation algorithm. Additionally, we discuss an appropriate interpretation of Shannon information which allows us to measure the amount of information contained in a model, and use this interpretation to measure the amount of information introduced during data assimilation-based system identification. Finally, we propose a generalization of the ensemble Kalman filter designed to alleviate one of the primary assumptions - that the observation function is linear.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectData Assimilationen_US
dc.subjectHidden Markov Modelsen_US
dc.subjectInformation Theoryen_US
dc.subjectRemote Sensingen_US
dc.subjectSoil Moistureen_US
dc.subjectHydrologyen_US
dc.subjectBayesian Analysisen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGupta, Hoshin V.en_US
dc.contributor.committeememberFerré, Ty Paul A.en_US
dc.contributor.committeememberWinter, C. Larryen_US
dc.contributor.committeememberCrow, Wade T.en_US
dc.contributor.committeememberGupta, Hoshin V.en_US
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