| DOI: | 10.2172/1237006
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| 报告号: | DOE-UTAUSTIN--0002710
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| 报告题名: | Uncertainty Quantification for Large-Scale Ice Sheet Modeling |
| 作者: | Ghattas, Omar
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| 出版年: | 2016
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| 发表日期: | 2016-02-05
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| 总页数: | 11
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| 国家: | 美国
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| 语种: | 英语
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| 英文摘要: | This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean. |
| URL: | http://www.osti.gov/scitech/servlets/purl/1237006
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| Citation statistics: |
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| 资源类型: | 研究报告
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/42275
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| Appears in Collections: | 过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响
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| 1237006.pdf（341KB） | 研究报告 | -- | 开放获取 | | View
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| Recommended Citation: | |
Ghattas, Omar. Uncertainty Quantification for Large-Scale Ice Sheet Modeling. 2016-01-01.
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