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挡，摩擦力使冰川不会立即滑入海洋。摩擦力有两种：一种是冰柱内部的内耗，它可以抵抗冰的变形；另一种是底部的摩擦力，它可以抵抗冰在基岩上滑动。很难区分内部摩擦和基础摩擦，因为两者在数据收集的最常见目标--冰盖表面上都有相似的表述。然而，理解这种划分很重要，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发展。该项目将来自冰盖表面的星载遥感观测与对冰盖内部结构成像的穿冰雷达数据结合在一起，以便划分每个摩擦源的贡献。这些结果将促进对冰流的基本理解，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社区的数据再利用；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)开发提取冰盖地层学的新开源算法；2)创建可用于未来冰层运动研究的地层学数据产品；3)使用统计分析，将雷达数据集与冰面速度、测高、气候变量和基于模型的基本摩擦的遥感数据集整合到更大范围的解释中。作为这项工作的一部分而开发的计算工具将被整合并作为可重复使用的软件工具包发布，用于穿冰雷达数据分析。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界面，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。在一些地区，成像的冰川地层学将偏离仅基于表面测量的预期冰动力环境。在那里，内部动力学(或冰动力历史)与表面动力学不一致，可能是因为内部摩擦约束较差，错误地归因于基础摩擦。这项工作将开发数据和统计工具，以约束来自冰穿透雷达的内耗，使这些数据产品和工具可用于未来的工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.