A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of poster...

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Published in:The Cryosphere
Main Authors: Gopalan, Giridhar Raja, Hrafnkelsson, Birgir, Adalgeirsdottir, Gudfinna, Jarosch, Alexander H., Pálsson, Finnur
Other Authors: Raunvísindadeild (HÍ), Faculty of Physical Sciences (UI), Jarðvísindastofnun (HÍ), Institute of Earth Sciences (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus GmbH 2018
Subjects:
Online Access:https://hdl.handle.net/20.500.11815/815
https://doi.org/10.5194/tc-12-2229-2018
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spelling ftopinvisindi:oai:opinvisindi.is:20.500.11815/815 2023-05-15T18:32:09+02:00 A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions Gopalan, Giridhar Raja Hrafnkelsson, Birgir Adalgeirsdottir, Gudfinna Jarosch, Alexander H. Pálsson, Finnur Raunvísindadeild (HÍ) Faculty of Physical Sciences (UI) Jarðvísindastofnun (HÍ) Institute of Earth Sciences (UI) Verkfræði- og náttúruvísindasvið (HÍ) School of Engineering and Natural Sciences (UI) Háskóli Íslands University of Iceland 2018-07-11 2229-2248 https://hdl.handle.net/20.500.11815/815 https://doi.org/10.5194/tc-12-2229-2018 en eng Copernicus GmbH The Cryosphere;12(7) https://www.the-cryosphere.net/12/2229/2018/tc-12-2229-2018.pdf Gopalan, G., Hrafnkelsson, B., Aðalgeirsdóttir, G., Jarosch, A. H., and Pálsson, F.: A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions, The Cryosphere, 12, 2229-2248, https://doi.org/10.5194/tc-12-2229-2018, 2018. 1994-0416 1994-0424 (eISSN) https://hdl.handle.net/20.500.11815/815 The Cryosphere doi:10.5194/tc-12-2229-2018 info:eu-repo/semantics/openAccess Jöklafræði Jöklarannsóknir Íshvel info:eu-repo/semantics/article 2018 ftopinvisindi https://doi.org/20.500.11815/815 https://doi.org/10.5194/tc-12-2229-2018 2022-11-18T06:51:38Z Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier. The Icelandic Research Fund (RANNIS) is thanked for funding this research. Peer Reviewed Article in Journal/Newspaper The Cryosphere Opin vísindi (Iceland) The Cryosphere 12 7 2229 2248
institution Open Polar
collection Opin vísindi (Iceland)
op_collection_id ftopinvisindi
language English
topic Jöklafræði
Jöklarannsóknir
Íshvel
spellingShingle Jöklafræði
Jöklarannsóknir
Íshvel
Gopalan, Giridhar Raja
Hrafnkelsson, Birgir
Adalgeirsdottir, Gudfinna
Jarosch, Alexander H.
Pálsson, Finnur
A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
topic_facet Jöklafræði
Jöklarannsóknir
Íshvel
description Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier. The Icelandic Research Fund (RANNIS) is thanked for funding this research. Peer Reviewed
author2 Raunvísindadeild (HÍ)
Faculty of Physical Sciences (UI)
Jarðvísindastofnun (HÍ)
Institute of Earth Sciences (UI)
Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Háskóli Íslands
University of Iceland
format Article in Journal/Newspaper
author Gopalan, Giridhar Raja
Hrafnkelsson, Birgir
Adalgeirsdottir, Gudfinna
Jarosch, Alexander H.
Pálsson, Finnur
author_facet Gopalan, Giridhar Raja
Hrafnkelsson, Birgir
Adalgeirsdottir, Gudfinna
Jarosch, Alexander H.
Pálsson, Finnur
author_sort Gopalan, Giridhar Raja
title A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
title_short A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
title_full A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
title_fullStr A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
title_full_unstemmed A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
title_sort bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions
publisher Copernicus GmbH
publishDate 2018
url https://hdl.handle.net/20.500.11815/815
https://doi.org/10.5194/tc-12-2229-2018
genre The Cryosphere
genre_facet The Cryosphere
op_relation The Cryosphere;12(7)
https://www.the-cryosphere.net/12/2229/2018/tc-12-2229-2018.pdf
Gopalan, G., Hrafnkelsson, B., Aðalgeirsdóttir, G., Jarosch, A. H., and Pálsson, F.: A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions, The Cryosphere, 12, 2229-2248, https://doi.org/10.5194/tc-12-2229-2018, 2018.
1994-0416
1994-0424 (eISSN)
https://hdl.handle.net/20.500.11815/815
The Cryosphere
doi:10.5194/tc-12-2229-2018
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/20.500.11815/815
https://doi.org/10.5194/tc-12-2229-2018
container_title The Cryosphere
container_volume 12
container_issue 7
container_start_page 2229
op_container_end_page 2248
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