Collaborative Research: GEO OSE Track 1: Advanced cloud-based Data- and Visualization-Integrated Simulation EnviRonment (ADVISER) to Advance Computational Glaciology

合作研究：GEO OSE Track 1：先进的基于云的数据和可视化集成模拟环境 (ADVISER)，以推进计算冰川学

• 批准号: 2324736
• 负责人: Daniel Shapero
• 金额: $ 4.67万
• 依托单位: University of Washington
• 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: 2023-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324736&HistoricalAwards=false
• 关键词: Collaborative; Research; GEO; OSE; Track

The adoption of cloud computing has increased tremendously over the years, especially in business. However, the use of this technology in computational geosciences—and particularly, in polar sciences and glaciology—has lagged behind. For scientists working in these areas, cloud computing can greatly simplify how users access and leverage high-performance computing resources, as well as open source multiphysics simulation software. To realize these benefits, it is necessary to create open, inclusive cyberinfrastructure that bridges existing knowledge gaps and to conduct outreach to improve adoption of the cloud and existing geosciences software platforms. This project achieves these goals by: (1) building an open cloud computing platform, Advanced cloud-based Data and Visualization Integrated Simulation EnviRonment (ADVISER), that can be used by computational geoscientists; and (2) by extending existing geosciences software (Icepack) to run easily and efficiently on ADVISER. Extending ADVISER for use in computational glaciology research and education democratizes the power of cloud computing and gives its benefits to those with little or no expertise in the cloud. By its design, no special hardware or knowledge is required to use ADVISER, so students in classroom environments will be able to participate in new kinds of lesson plans, such as running glacier flow simulations on ADVISER during class sessions. Through a planned workshop/hackathon, this project will involve students across several universities and colleges, educating them to better harness computational power (via the cloud), a skillset not traditionally available through course curricula in geosciences or engineering.The project comprises four specific tasks: (1) extending a cloud-native platform, ADVISER, for running and managing scientific software and data in the cloud; (2) adapting the Icepack software for modeling of glacier flow to run on ADVISER; (3) creating data assimilation pipelines that enable importing various existing and novel data sets into the ADVISER platform in formats amenable to usage in software like Icepack; and (4) hosting a workshop and a hackathon that spur sustainable adoption and extensions of ADVISER and Icepack for the broader computational geosciences community. The investigators' vision is that their ADVISER platform will allow for the seamless incorporation of emerging remote sensing data, machine learning algorithms, and physics-based computational models for forward and inverse problems. The investigators will particularly focus on real-world data assimilation from Antarctic ice shelves.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.

多年来，云计算的采用率大幅增加，尤其是在商业领域。然而，这项技术在计算地球科学中的应用，特别是在极地科学和冰川学中的应用已经落后了。对于在这些领域工作的科学家来说，云计算可以大大简化用户访问和利用高性能计算资源以及开源多物理场仿真软件的方式。为了实现这些好处，有必要建立开放、包容的网络基础设施，弥合现有的知识差距，并开展外联活动，以提高云和现有地球科学软件平台的采用率。该项目通过以下两个方面实现了这些目标：（1）建立一个开放的云计算平台，高级基于云的数据和可视化集成仿真环境（ADVISER），可供计算地球科学家使用;（2）扩展现有的地球科学软件（Icepack），使其能够轻松有效地在ADVISER上运行。将ADVISER扩展到计算冰川学研究和教育中，可以使云计算的力量民主化，并使那些在云计算方面很少或没有专业知识的人受益。通过其设计，使用ADVISER不需要特殊的硬件或知识，因此教室环境中的学生将能够参与新类型的课程计划，例如在课堂上运行ADVISER上的冰川流模拟。通过计划的研讨会/黑客活动，该项目将涉及几所大学和学院的学生，教育他们更好地利用计算能力该项目包括四项具体任务：（1）扩展云原生平台ADVISER，用于在云中运行和管理科学软件和数据;（2）调整用于冰川流动建模的Icepack软件，使其在ADVISER上运行;（3）创建数据同化管道，以便将各种现有的和新的数据集以适合在Icepack等软件中使用的格式导入ADVISER平台;以及（4）举办研讨会和黑客活动，以促进ADVISER和Icepack在更广泛的计算地球科学界的可持续采用和扩展。研究人员的愿景是，他们的ADVISER平台将允许无缝整合新兴的遥感数据，机器学习算法和基于物理的计算模型，用于正向和反向问题。该奖项反映了NSF的法定使命，并被认为值得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来支持。