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

冰流受到摩擦力的阻力，使冰川不会立即滑入海洋。摩擦有两种：一种是防止冰变形的冰柱内部摩擦，另一种是防止冰在基岩上滑动的基底摩擦。内部摩擦和基础摩擦之间的划分是困难的，因为两者在数据收集的最常见目标-冰盖表面具有相似的表达式。然而，理解这种划分是很重要的，因为控制内部和基础摩擦的物理过程在不同的时间尺度上起作用和进化。该项目将来自冰盖表面的星载遥感观测与冰层穿透雷达数据相结合，后者对冰盖的内部结构进行成像，以便划分每个摩擦源的贡献。研究结果将促进对冰流的基本认识，并将加强对未来海平面上升的预测。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。研究人员将通过以下方式扩大现有冰层穿透数据集的影响：1)开发新的开源算法来提取冰川地层学；2)创建可用于未来冰运动研究的地层数据产品；3)利用统计分析将雷达数据集与冰面速度、海拔、气候变量和模式导出的基础摩擦等遥感数据集整合到更大规模的解译中。作为这项工作的一部分开发的计算工具将作为一个可重复使用的软件工具包集成并发布，用于冰穿透雷达数据分析。该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，将其加载到统一框架中，通过预定义的处理流程进行步骤，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的冰动力背景。在那里，内部动力学（或冰动力历史）与表面动力学不一致，可能是因为内摩擦约束不佳，而被错误地归因于基底摩擦。这项工作将开发用于限制破冰雷达内耗的数据和统计工具，使这些数据产品和工具可用于今后的工作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.