Collaborative Research: EarthCube Data Capabilities: Project Pythia: A Community Learning Resource for Geoscientists

协作研究：EarthCube 数据功能：Pythia 项目：地球科学家的社区学习资源

• 批准号: 2026863
• 负责人: Brian Rose
• 金额: $ 37.93万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026863&HistoricalAwards=false
• 关键词: Collaborative; Research; EarthCube; Data; Capabilities

Project Pythia will provide a public, web-accessible training resource that will help educate current, and aspiring, earth scientists to more effectively use both the Scientific Python Ecosystem and Cloud Computing to make sense of huge volumes of numerical scientific data. Numerical simulations run on supercomputers are used in the study of climate, weather, atmospheric chemistry, wildfires, space weather, and more. Similarly, a tremendous volume of digital data produced by numerical simulations, or observations made with instruments, are analyzed with the help of powerful computers and software. Scientists thus need to be able to effectively analyze, manipulate, and make sense of potentially vast volumes of data. This project focuses on two technologies that have emerged and are being adopted by scientific communities relatively recently: Cloud Computing platforms and a software ecosystem of scientific tools built around the open source programming language called Python. Project Pythia will construct and deploy a trusted, community-oriented education and training resource to improve computing literacy in all areas relevant to the analysis of geoscientific data, with a particular emphasis on moving workflows to the Cloud. Project Pythia will leverage Jupyter Notebooks as the primary delivery mechanism for training examples, curricula, and as an interactive computing platform. The content for Project Pythia will be hosted on GitHub and maintained using an Open Development model that will facilitate and encourage contributions from a broad user community, as well as help ensure the long-term sustainability of the project. Content development and new user growth will be catalyzed through data-intensive classroom and research activities at SUNY at Albany. The result will be one of the world's go-to, community-owned sites for geoscientists and students who want to know what tools to use and how to use them to explore geoscience data.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.

Pythia 项目将提供一个可通过网络访问的公共培训资源，帮助教育当前和有抱负的地球科学家更有效地使用科学 Python 生态系统和云计算来理解大量的数值科学数据。在超级计算机上运行的数值模拟用于气候、天气、大气化学、野火、太空天气等的研究。同样，通过数值模拟或仪器观察产生的大量数字数据，可以在功能强大的计算机和软件的帮助下进行分析。因此，科学家需要能够有效地分析、操纵和理解潜在的大量数据。该项目重点关注最近出现并被科学界采用的两项技术：云计算平台和围绕称为 Python 的开源编程语言构建的科学工具软件生态系统。 Pythia 项目将构建和部署一个值得信赖的、面向社区的教育和培训资源，以提高与地球科学数据分析相关的所有领域的计算素养，特别强调将工作流程转移到云端。 Pythia 项目将利用 Jupyter Notebooks 作为培训示例、课程的主要交付机制，并作为交互式计算平台。 Pythia 项目的内容将托管在 GitHub 上，并使用开放开发模型进行维护，该模型将促进和鼓励广大用户社区的贡献，并帮助确保项目的长期可持续性。纽约州立大学奥尔巴尼分校的数据密集型课堂和研究活动将促进内容开发和新用户增长。其结果将成为地球科学家和学生想要知道使用什么工具以及如何使用它们来探索地球科学数据的世界首选社区网站之一。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。

项目成果

The Arctic Surface Heating Efficiency of Tropospheric Energy Flux Events

对流层能量通量事件的北极表面加热效率

• DOI: 10.1175/jcli-d-21-0852.1
• 发表时间: 2022-09
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Cardinale, Christopher J.;Rose, Brian E.
• 通讯作者: Rose, Brian E.

Mean State AMOC Affects AMOC Weakening through Subsurface Warming in the Labrador Sea

平均状态 AMOC 通过拉布拉多海次表层变暖影响 AMOC 减弱

• DOI: 10.1175/jcli-d-22-0464.1
• 发表时间: 2023-06
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Lin, Yuan;Rose, Brian E. J.;Hwang, Yen
• 通讯作者: Hwang, Yen

The Increasing Efficiency of the Poleward Energy Transport Into the Arctic in a Warming Climate

• DOI: 10.1029/2022gl100834
• 发表时间: 2023-01-09
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: C. Cardinale;B. Rose
• 通讯作者: B. Rose