Postdoctoral Fellowship: OPP-PRF: Disentangling Ice-sheet Internal and Basal Processes through Novel Ice-penetrating Radar Integration Built on Scalable, Cloud-based Infrastructure

博士后奖学金：OPP-PRF：通过基于可扩展、基于云的基础设施构建的新型透冰雷达集成来解开冰盖内部和基础过程

• 批准号: 2317927
• 负责人: Benjamin Hills
• 金额: $ 31.78万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317927&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; OPP; PRF; Disentangling

Ice flow is resisted by frictional forces that keep a glacier from immediately sliding into the ocean. Friction comes in two varieties: internal friction within the ice column which resists ice deformation and basal friction which resists ice sliding over its bedrock substrate. Partitioning between internal and basal friction is difficult since both have similar expressions at the most common target for data collection—the ice-sheet surface. However, understanding this partitioning is important because the physical processes that control internal and basal friction act and evolve at different timescales. This project combines spaceborne remote sensing observations from the ice-sheet surface with ice-penetrating radar data that images the internal structure of the ice sheet in order to partition the contribution of each source of friction. Results will advance the fundamental understanding of ice flow and will strengthen projections of future sea-level rise. Broader Impacts of the project include facilitating data reuse for the ice-sheet research community; the strategy for distributing the software toolkit includes student mentorship and hackathon teaching.The researcher will expand the impact of existing ice-penetrating datasets by 1) developing new open-source algorithms for extraction of englacial stratigraphy; 2) creating stratigraphy data products that can be assimilated into future studies of ice motion; and 3) using statistical analyses to integrate radar datasets into larger-scale interpretations with remote sensing datasets of ice-surface velocity, altimetry, climate variables, and model-derived basal friction. The computational tools developed as part of this effort will be integrated and released as a reusable software toolkit for ice-penetrating radar data analysis. The toolkit will be validated and tested by deployment to cloud-hosted JupyterHub instances, which will serve as a singular interface to access radar and remote sensing data, load them into a unified framework, step through a predefined processing flow, and carry out statistical analyses. In some areas, the imaged englacial stratigraphy will deviate from the ice-dynamic setting expected based on surface measurements alone. There, the internal dynamics (or ice-dynamic history) are inconsistent with the surface dynamics, likely because internal friction is poorly constrained and misattributed to basal friction instead. This work will develop the data and statistical tools for constraining internal friction from ice-penetrating radar, making those data products and tools available for future work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

冰流受到摩擦力的抵抗，使冰川不会立即滑入海洋。摩擦力有两种：冰柱内的内部摩擦力，其抵抗冰变形；基底摩擦力，其抵抗冰在其基岩基底之间滑动。基础摩擦力很困难，因为两者在最常见的数据收集目标（冰盖表面）上有相似的表达方式。然而，理解这种划分很重要，因为控制内部摩擦力和基础摩擦力的物理过程在不同的时间尺度上起作用和演变。联合起来利用透冰雷达数据对冰盖表面进行星载遥感观测，对冰盖的内部结构进行成像，以便区分每种摩擦力来源的贡献，结果将增进对冰流的基本了解，并加强预测。该项目的更广泛影响包括促进冰盖研究界的数据重用；分发软件工具包的策略包括学生指导和黑客马拉松教学。经过1) 开发用于提取冰川地层的新开源算法；2) 创建可用于未来冰运动研究的地层数据产品；3) 使用统计分析将雷达数据集整合到遥感数据集的更大规模解释中。作为这项工作的一部分开发的计算工具将被集成并作为可重复使用的软件工具包发布，用于破冰雷达数据分析。工具包将通过部署到云托管的 JupyterHub 实例进行验证和测试，该实例将作为访问雷达和遥感数据的单一接口，将它们加载到统一框架中，逐步执行预定义的处理流程，并进行统计分析。在某些地区，成像的冰川地层学将偏离仅基于表面测量预期的冰动力学设置。在那里，内部动力学（或冰动力学历史）与表面动力学不一致，可能是因为内部摩擦力受到较差的约束和。这项工作将开发反映限制穿冰雷达内摩擦的数据和统计工具，使这些数据产品和工具可用于未来的工作。该奖项是法定任务，并通过评估被认为值得支持。利用基金会的智力优势和更广泛的影响审查标准。