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: 2324737
• 负责人: Catherine Walker
• 金额: $ 5.46万
• 依托单位: Woods Hole Oceanographic Institution
• 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=2324737&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，云计算的采用大幅增加，尤其是在商业领域，但是，该技术在计算地球科学（尤其是极地科学和冰川学）中的使用却落后于这些领域的科学家。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。为了实现这些优势，有必要创建开放、包容性的网络基础设施来弥补现有的知识差距，并进行推广以提高其采用率。云和该项目通过以下方式实现这些目标：(1) 构建可供计算地球科学家使用的开放云计算平台、基于云的高级数据和可视化集成模拟环境 (ADVISER)；以及 (2) 通过扩展现有的地球科学软件 (Icepack) 可在 ADVISER 上轻松高效地运行 扩展 ADVISER 以用于计算冰川学研究和教育，使云计算的力量大众化，并为人们带来好处。根据其设计，使用 ADVISER 不需要特殊的硬件或知识，因此课堂环境中的学生将能够参与新型课程计划，例如在 ADVISER 上运行冰川流模拟。通过计划中的研讨会/黑客马拉松，该项目将让多所大学和学院的学生参与其中，教育他们更好地利用计算能力（通过云），这是传统上通过地球科学或工程课程无法获得的技能。包括四个具体任务：（1）扩展云原生平台 ADVISER，用于在云中运行和科学管理软件和数据；（2）调整用于冰川流建模的 Icepack 软件以在 ADVISER 上运行；同化管道，能够以适合 Icepack 等软件使用的格式将各种现有和新颖的数据集导入 ADVISER 平台；(4) 举办研讨会和黑客马拉松，促进可持续采用和扩展； ADVISER 和 Icepack 面向更广泛的计算地球科学界，研究人员的愿景是他们的 ADVISER 平台将允许无缝整合新兴遥感数据、机器学习算法和基于物理的计算模型，特别关注正向和逆向问题。来自南极冰架的真实世界数据同化。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。