HDR Institute: HARP- Harnessing Data and Model Revolution in the Polar Regions

HDR 研究所：HARP——利用极地地区的数据和模型革命

• 批准号: 2118285
• 负责人: Vandana Janeja
• 金额: $ 1300万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118285&HistoricalAwards=false
• 关键词: HDR; Institute; HARP; Harnessing; Data

Climate-change induced loss of polar ice sheets impacts many lives and increases coastal flooding by rising sea level and affecting ocean circulation. However, it remains difficult to accurately predict how quickly the ice sheets will continue to shrink. In particular, we are still challenged by a limited understanding of transdisciplinary processes that determine ice sheet change, such as the role of subglacial topography and ice-atmosphere-ocean interactions. Timely investment in machine learning and data intensive research can revolutionize the way that scientists currently answer questions related to ice dynamics. This HDR Institute serves as a research hub where experts in data science, Arctic and Antarctic science, and cyberinfrastructure in academia, government, and private sectors come together to develop transformative and integrative data science solutions to reduce uncertainties in projecting future sea-level rise and climate change. i-HARP researchers investigate the potential of novel physics-aware data science and machine learning approaches to address national priorities and challenges on Navigating the New Arctic, climate change, and sea-level rise.The HDR Institute aims to harness massive heterogeneous, noisy, and discontinuous data in space and time and integrate data with numerical and physical models. Researchers at i-HARP are investigating novel data science techniques including deep generative adversarial networks, graph neural networks, meta learning, hybrid networks, physics-informed machine learning, causal artificial intelligence, data assimilation, spatiotemporal deep learning, and scalable algorithms. Due to the fundamental nature of data science problems that i-HARP addresses, the solutions can be translated to other disciplines such as remote sensing, medicine, and autonomous driving. Moreover, the convergence team champions multiple clusters of research-integrated educational initiatives, with a specific focus on facilitating cross-disciplinary collaborations, training next-generation multi-disciplinary researchers and engaging the public in scientific inquiry as related to climate change and data science. In partnership with related communities, i-HARP designs curricula, and offers hands-on community workshops, lecture series, conference tutorials, and training. i-HARP engages students from underrepresented minority groups by leveraging several existing organizations for underrepresented minorities.This project is part of the National Science Foundation's Big Idea activities in Harnessing the Data Revolution (HDR). This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Section for Antarctic Sciences and the Section for Arctic Sciences within the NSF Office of Polar Programs.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.

气候变化引起的极地冰盖损失影响了许多人的生命，并通过海平面上升和影响海洋环流增加了沿海洪水。然而，仍然很难准确预测冰盖将继续缩小的速度。特别是，我们仍然面临着对决定冰盖变化的跨学科过程的有限理解的挑战，例如冰下地形的作用和冰-大气-海洋相互作用。对机器学习和数据密集型研究的及时投资可以彻底改变科学家目前回答与冰动力学相关问题的方式。该 HDR 研究所是一个研究中心，数据科学、北极和南极科学以及学术界、政府和私营部门的网络基础设施专家齐聚一堂，开发变革性和综合性的数据科学解决方案，以减少预测未来海平面上升和下降的不确定性。气候变化。 i-HARP 研究人员研究了新颖的物理感知数据科学和机器学习方法的潜力，以解决新北极、气候变化和海平面上升方面的国家优先事项和挑战。HDR 研究所旨在利用大规模异构、嘈杂、以及空间和时间上的不连续数据，并将数据与数值和物理模型集成。 i-HARP 的研究人员正在研究新颖的数据科学技术，包括深度生成对抗网络、图神经网络、元学习、混合网络、物理信息机器学习、因果人工智能、数据同化、时空深度学习和可扩展算法。由于 i-HARP 解决的数据科学问题的基本性质，这些解决方案可以转化为其他学科，例如遥感、医学和自动驾驶。此外，融合团队倡导多个研究整合教育举措，特别注重促进跨学科合作、培训下一代多学科研究人员以及让公众参与与气候变化和数据科学相关的科学探究。 i-HARP 与相关社区合作设计课程，并提供实践社区研讨会、系列讲座、会议教程和培训。 i-HARP 通过利用几个针对代表性不足的少数群体的现有组织来吸引来自代表性不足的少数群体的学生。该项目是美国国家科学基金会利用数据革命 (HDR) 的大创意活动的一部分。 该奖项由先进网络基础设施办公室颁发，并得到 NSF 极地项目办公室南极科学科和北极科学科的共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

Mobile Augmented Reality System for Emergency Response

用于应急响应的移动增强现实系统

• 发表时间: 2023
• 期刊: Management and Applications (SERA 2023
• 作者: Sharma, S

Metrics for the quality and consistency of ice layer annotations

冰层注释的质量和一致性指标

• 发表时间: 2023
• 期刊: IEEE International Geoscience and Remote Sensing Symposium
• 作者: N. Tack;B. A. Tama;A. Jebeli;V. Janeja;D. Engel;R. Williams
• 通讯作者: R. Williams

TSSA: two-step semi-supervised annotation for englacial radargrams on the Greenland ice sheet

TSSA：格陵兰冰盖冰川雷达图的两步半监督注释

• 发表时间: 2023
• 期刊: IEEE International Geoscience and Remote Sensing Symposium
• 作者: A. Jebeli;B. A. Tama;V. Janeja;N. Holschuh;C. Jensen;M. Morlighem;J. A MacGregor;M. Fahnestock
• 通讯作者: M. Fahnestock

Enhanced Deep Learning Super-Resolution for Bathymetry Data

• DOI: 10.1109/bdcat56447.2022.00014
• 发表时间: 2022-12
• 期刊: 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
• 作者: Xingyan Li;Jian Li;Zachary Williams;Xin Huang;M. Carroll;Jianwu Wang

Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

评估格陵兰冰下床地形的机器学习和统计模型

• DOI: 10.1109/icmla58977.2023.00097
• 发表时间: 2023
• 期刊: IEEE
• 作者: Yi, Katherine;Dewar, Angelina;Tabassum, Tartela;Lu, Jason;Chen, Ray;Alam, Homayra;Faruque, Omar;Li, Sikan;Morlighem, Mathieu;Wang, Jianwu
• 通讯作者: Wang, Jianwu