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.

该项目设计并实现了一个用于处理PB级数据集的软件框架;重点是全球海洋环流。 一个由三所大学（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，能够以1公里的水平分辨率提供全球海洋环流模型输出。该产品将托管在一个开放的门户网站上，为社区提供可扩展的软件工具，以便对数据集进行分析。该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具体问题。这项工作的目标是创建和演示一个完整的和可复制的网络基础设施，用于共享和分析大规模模拟。 重点是高分辨率的海洋环流建模，软件工具将实现有效的存储。海洋和气候动力学研究的两个主要挑战是解决：处理高分辨率模拟的大型数据集，并了解小尺度海洋过程在大尺度海洋/气候系统中的作用。 解决第一个挑战将大大促进目前和今后对海洋/大气/气候系统的研究;解决第二个挑战将大大提高对海洋/气候动态的认识。该项目建立了一个统一的数据系统，包括高分辨率的全球海洋环流模拟，用于数据共享的千万亿次门户网站和可扩展的交互式分析软件工具。 该项目的软件框架预计将为用户处理千万亿次到百万兆次的数据集。 该项目利用了几个已有的能力：东格陵兰大陆斜坡溢出射流的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，以及从东北存储交换多PB区域数据存储中吸取的经验教训。 更广泛的目标是地球科学和其他学科的下一代模拟软件。 该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由海洋科学部和美国国家科学基金会地球科学理事会的综合与合作教育和研究计划共同支持。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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

Impact of Vertical Mixing Parameterizations on Internal Gravity Wave Spectra in Regional Ocean Models

垂直混合参数化对区域海洋模型内重力波谱的影响

• DOI: 10.1029/2022gl099614
• 发表时间: 2022
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Thakur, Ritabrata;Arbic, Brian K.;Menemenlis, Dimitris;Momeni, Kayhan;Pan, Yulin;Peltier, W. R.;Skitka, Joseph;Alford, Matthew H.;Ma, Yuchen
• 通讯作者: Ma, Yuchen