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dc.contributor.authorCioaca, Alexandru Georgeen_US
dc.date.accessioned2015-04-24T08:02:20Z
dc.date.available2015-04-24T08:02:20Z
dc.date.issued2013-09-04en_US
dc.identifier.othervt_gsexam:1606en_US
dc.identifier.urihttp://hdl.handle.net/10919/51795
dc.description.abstractA deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimilation is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) - data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as to reducing the operating costs of measuring networks, while preserving their ability to capture the essential features of the system under consideration.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectdata assimilationen_US
dc.subjectdynamic data-driven problemen_US
dc.subjectsecond-order adjointsen_US
dc.subjectadaptive observationsen_US
dc.subjectsensor placementen_US
dc.subjectintelligent sensorsen_US
dc.subjectsensitivity analysisen_US
dc.subjectuncertainty quantificationen_US
dc.subjectnonlinear optimizationen_US
dc.subjectinverse problemsen_US
dc.subjectparameter estimationen_US
dc.subjectmatrix-free linear solversen_US
dc.subjecttruncated singular value decompositionen_US
dc.titleA Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilationen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairSandu, Adrianen_US
dc.contributor.committeememberShaffer, Clifford A.en_US
dc.contributor.committeememberRibbens, Calvin J.en_US
dc.contributor.committeememberDe Sturler, Ericen_US
dc.contributor.committeememberIliescu, Traianen_US


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