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Multi-parameter estimation in glacier models with adjoint and algorithmic differentiation

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dc.contributor.advisor Patrick Heimbach. en_US Davis, Andrew D. (Andrew Donaldson) en_US
dc.contributor.other Massachusetts Institute of Technology. Computation for Design and Optimization Program. en_US 2012-09-13T18:57:57Z 2012-09-13T18:57:57Z 2012 en_US 2012 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2012. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 75-77). en_US
dc.description.abstract The cryosphere is comprised of about 33 million km³ of ice, which corresponds to 70 meters of global mean sea level equivalent [30]. Simulating continental ice masses, such as the Antarctic or Greenland Ice Sheets, requires computational models capturing abrupt changes in ice sheet dynamics, which are still poorly understood. Input parameters, such as basal drag and topography, have large effects on the applied stress and flow fields but whose direct observation is very difficult, if not impossible. Computational methods are designed to aid in the development of ice sheet models, ideally identifying the relative importance of each parameter and formulating inverse methods to infer uncertain parameters and thus constrain ice sheet flow. Efficient computation of the tangent linear and adjoint models give researchers easy access to model derivatives. The adjoint and tangent linear models enable efficient global sensitivity computation and parameter optimization on unknown or uncertain ice sheet properties, information used to identify model properties having large effects on sea-level. The adjoint equations are not always easily obtained analytically and often require discretizing additional PDE's. Algorithmic differentiation (AD) decomposes the model into a composite of elementary operations (+, -, *, /, etc ... ) and a source-to-source transformation generates code for the Jacobian and its transpose for each operations. Derivatives computed using the tangent linear and adjoint models, with code generated by AD, are applied to parameter estimation and sensitivity analysis of simple glacier models. AD is applied to two examples, equations describing changes in borehole temperature over time and instantaneous ice velocities. Borehole model predictions and data are compared to infer paleotemperatures, geothermal heat flux, and physical ice properties. Inversion using adjoint methods and AD increases the control space, allowing inference for all uncertain parameters. The sensitivities of ice velocities to basal friction and basal topography are compared. The basal topography has significantly larger sensitivities, suggesting it plays a larger role in flow dynamics and future work should seek to invert for this parameter. en_US
dc.description.statementofresponsibility by Andrew D. Davis. en_US
dc.format.extent 77 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Computation for Design and Optimization Program. en_US
dc.title Multi-parameter estimation in glacier models with adjoint and algorithmic differentiation en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Computation for Design and Optimization Program. en_US
dc.identifier.oclc 808368448 en_US

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