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碍，摩擦力使冰川不会立即滑入海洋。摩擦力有两种：冰柱内部的内部摩擦力，它阻止冰的变形;底部摩擦力，它阻止冰在基岩基底上滑动。划分内部摩擦力和基底摩擦力是困难的，因为两者在最常见的数据收集目标冰盖表面上具有相似的表达式。然而，理解这种划分是重要的，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演变。该项目将从冰盖表面进行的空间遥感观测与对冰盖内部结构进行成像的探冰雷达数据相结合，以便划分每种摩擦源的作用。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内地层; 2）创建可以被吸收到未来冰运动研究中的地层数据产品; 3）利用统计分析将雷达数据集与冰面速度、测高、气候变量和模型导出的基底摩擦的遥感数据集结合起来进行更大尺度的解释。作为这项工作的一部分开发的计算工具将被集成和发布，作为一个可重复使用的软件工具包，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ApplyterHub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，将其加载到统一的框架中，逐步完成预定义的处理流程，并进行统计分析。在某些地区，成像的冰内地层学将偏离仅根据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约束不好，而被错误地归因于基底摩擦力。这项工作将开发数据和统计工具，以限制来自冰穿透雷达的内部摩擦，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，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.