Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning's friction coefficient

Handle URI:
http://hdl.handle.net/10754/563473
Title:
Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning's friction coefficient
Authors:
Mayo, Talea; Butler, Troy; Dawson, Clint N.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Coastal ocean models play a major role in forecasting coastal inundation due to extreme events such as hurricanes and tsunamis. Additionally, they are used to model tides and currents under more moderate conditions. The models numerically solve the shallow water equations, which describe conservation of mass and momentum for processes with large horizontal length scales relative to the vertical length scales. The bottom stress terms that arise in the momentum equations can be defined through the Manning's n formulation, utilizing the Manning's n coefficient. The Manning's n coefficient is an empirically derived, spatially varying parameter, and depends on many factors such as the bottom surface roughness. It is critical to the accuracy of coastal ocean models, however, the coefficient is often unknown or highly uncertain. In this work we reformulate a statistical data assimilation method generally used in the estimation of model state variables to estimate this model parameter. We show that low-dimensional representations of Manning's n coefficients can be recovered by assimilating water elevation data. This is a promising approach to parameter estimation in coastal ocean modeling. © 2014 Elsevier Ltd.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Ocean Modelling
Issue Date:
Apr-2014
DOI:
10.1016/j.ocemod.2014.01.001
Type:
Article
ISSN:
14635003
Sponsors:
This work was supported by the King Abdullah University of Science and Technology and the Gulf of Mexico Research Initiative Center for Advanced Research on Transport of Hydrocarbons in the Environment. This support is gratefully acknowledged.
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMayo, Taleaen
dc.contributor.authorButler, Troyen
dc.contributor.authorDawson, Clint N.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T11:52:22Zen
dc.date.available2015-08-03T11:52:22Zen
dc.date.issued2014-04en
dc.identifier.issn14635003en
dc.identifier.doi10.1016/j.ocemod.2014.01.001en
dc.identifier.urihttp://hdl.handle.net/10754/563473en
dc.description.abstractCoastal ocean models play a major role in forecasting coastal inundation due to extreme events such as hurricanes and tsunamis. Additionally, they are used to model tides and currents under more moderate conditions. The models numerically solve the shallow water equations, which describe conservation of mass and momentum for processes with large horizontal length scales relative to the vertical length scales. The bottom stress terms that arise in the momentum equations can be defined through the Manning's n formulation, utilizing the Manning's n coefficient. The Manning's n coefficient is an empirically derived, spatially varying parameter, and depends on many factors such as the bottom surface roughness. It is critical to the accuracy of coastal ocean models, however, the coefficient is often unknown or highly uncertain. In this work we reformulate a statistical data assimilation method generally used in the estimation of model state variables to estimate this model parameter. We show that low-dimensional representations of Manning's n coefficients can be recovered by assimilating water elevation data. This is a promising approach to parameter estimation in coastal ocean modeling. © 2014 Elsevier Ltd.en
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology and the Gulf of Mexico Research Initiative Center for Advanced Research on Transport of Hydrocarbons in the Environment. This support is gratefully acknowledged.en
dc.publisherElsevier BVen
dc.subjectBottom stressen
dc.subjectData assimilationen
dc.subjectManning's nen
dc.subjectParameter estimationen
dc.subjectSEIK filteren
dc.subjectShallow water equationsen
dc.titleData assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning's friction coefficienten
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalOcean Modellingen
dc.contributor.institutionDepartment of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, United Statesen
dc.contributor.institutionInstitute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United Statesen
kaust.authorHoteit, Ibrahimen
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