Collaborative Research: Framework: Data: Toward Exascale Community Ocean Circulation Modeling

合作研究：框架：数据：迈向百万兆亿级社区海洋环流建模

• 批准号: 1835618
• 负责人: Paul O'Gorman
• 金额: $ 72.82万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835618&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Data; Toward

This project designs and implements a software framework for handling petabyte-scale datasets; the focus is on global ocean circulation. A team of three universities (Johns Hopkins University, MIT, and Columbia University) builds a unified data system that is capable of delivering global ocean circulation model output at 1 km horizontal resolution. The product will be hosted in an open portal, providing the community with scalable software tools to enable analysis of the dataset. The team will use this data to answer specific questions about mixing and dissipation processes in the ocean. The goal of this effort is the creation and demonstration of a complete and replicable cyberinfrastructure for sharing and analysis of massive simulations. The focus is on high resolution ocean circulation modeling, with software tools that will enable efficient storage. Two major challenges to the study of ocean and climate dynamics are addressed: handling large datasets from high-resolution simulations, and understanding the role of small-scale ocean processes in large-scale ocean/climate systems. Resolving the first challenge would significantly facilitate ongoing and future studies of the ocean/atmosphere/climate system; addressing the second challenge would profoundly improve understanding of ocean/climate dynamics. The project builds a unified data system consisting of high-resolution global ocean circulation simulations, a petascale portal for data sharing, and scalable software tools for interactive analysis. The software framework from this project is expected to handle petascale to exascale datasets for users. Several pre-existing capabilities are leveraged for this project: the JHU regional numerical model of the Spill Jet on the East Greenland continental slope, software from the Pangeo project, the SciServer data-intensive software infrastructure, and lessons learned from the North East Storage Exchange multi-petabyte regional data store. The broader target is next generation simulation software in the geosciences and other disciplines. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Ocean Sciences and the Integrative and Collaborative Education and Research Program within the NSF Directorate for Geosciences.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.

该项目设计并实施了用于处理PBABYTE规模数据集的软件框架；重点是全球海洋循环。 由三所大学（约翰·霍普金斯大学，麻省理工学院和哥伦比亚大学）组成的团队建立了一个统一的数据系统，该数据系统能够以1公里的水平分辨率提供全球海洋循环模型。该产品将托管在开放门户中，为社区提供可扩展的软件工具，以启用数据集分析。团队将使用这些数据来回答有关海洋中混合和耗散过程的特定问题。这项工作的目的是创建和演示一个完整且可复制的网络基础结构，用于共享和分析大规模模拟。 重点放在高分辨率的海洋循环建模上，并使用可以有效存储的软件工具。解决了海洋和气候动态研究的两个主要挑战：从高分辨率模拟中处理大型数据集，并了解小规模海洋过程在大型海洋/气候系统中的作用。 解决第一个挑战将显着促进海洋/气氛/气候系统的持续和未来研究；应对第二个挑战将深刻提高对海洋/气候动态的理解。该项目构建了一个统一的数据系统，该数据系统由高分辨率全球循环模拟，用于数据共享的Petascale门户以及用于交互式分析的可扩展软件工具。 该项目的软件框架预计将处理Petascale到用户的Exascale数据集。 该项目利用了几种预先存在的功能：东格陵兰大陆坡上的溢流喷气机的JHU区域数值模型，Pangeo项目的软件，Sciserver数据密集型软件基础设施，以及从North East Socedment Exchange中学到的经验教训多孔隙区域数据存储。 更广泛的目标是地球科学和其他学科中的下一代仿真软件。 NSF高级网络基础设施办公室的奖项得到了海洋科学系以及NSF地球科学局内的综合和协作教育与研究计划的共同支持。该奖项反映了NSF的法定任务，并被认为是通过使用评估值得的支持。基金会的智力优点和更广泛的影响审查标准。

项目成果

Local Air‐Sea Interactions at Ocean Mesoscale and Submesoscale in a Western Boundary Current

• DOI: 10.1029/2021gl097003
• 发表时间: 2022-03
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: E. Strobach;P. Klein;A. Molod;A. A. Fahad-A.;A. Trayanov;D. Menemenlis;H. Torres

High‐Frequency Submesoscale Motions Enhance the Upward Vertical Heat Transport in the Global Ocean

• DOI: 10.1029/2020jc016544
• 发表时间: 2020-09
• 期刊: Journal of Geophysical Research: Oceans
• 作者: Z. Su;H. Torres;P. Klein;A. Thompson;L. Siegelman;Jinbo Wang;D. Menemenlis;C. Hill

Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs

Oceananigans.jl：GPU 上快速且友好的地球物理流体动力学

• DOI: 10.21105/joss.02018
• 发表时间: 2020
• 期刊: Journal of Open Source Software
• 作者: Ramadhan, Ali;Wagner, Gregory;Hill, Chris;Campin, Jean-Michel;Churavy, Valentin;Besard, Tim;Souza, Andre;Edelman, Alan;Ferrari, Raffaele;Marshall, John
• 通讯作者: Marshall, John

Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision

• DOI: 10.1029/2020gl091363
• 发表时间: 2020-10
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: J. Yuval;P. O’Gorman;C. Hill

Three‐to‐Six‐Day Air–Sea Oscillation in Models and Observations

• DOI: 10.1029/2019gl085837
• 发表时间: 2020-05
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: E. Strobach;A. Molod;A. Trayanov;G. Forget;J. Campin;C. Hill;D. Menemenlis