Collaborative Research: EarthCube Capabilities: Raijin: Community Geoscience Analysis Tools for Unstructured Mesh Data

协作研究：EarthCube 功能：Raijin：非结构化网格数据的社区地球科学分析工具

• 批准号: 2126458
• 负责人: John Clyne
• 金额: $ 87.99万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126458&HistoricalAwards=false
• 关键词: Collaborative; Research; EarthCube; Capabilities; Raijin

The next generation of numerical models used to simulate the Earth’s climate and global weather represent a significant advancement in humanity’s ability to realistically and accurately model the Earth System. These new models solve complex mathematical equations that describe the motion, chemistry, and energy transfer of the planet’s oceans and atmosphere at spatial scales fine enough to capture individual storms. As a result, these so-called “Global Storm Resolving Models” (GSRMs) generate tremendous amounts of numerical data. Moreover, the structure of these data is far more complex than it is for current models. The goal of this work, Project Raijin, is to develop and provide the global weather and climate modeling communities with the software tools they need to make sense of these massive and complex data sets. Without the ability to effectively explore and interrogate GSRM outputs the value of these new models will be greatly limited. The tools created by Project Raijin will be developed under an open source license and made freely available to anyone in the world who wants to use, or even improve, them.The transition by the global weather and climate modeling communities to kilometer-scale resolutions enabled by unstructured grid “dynamical cores” comes with a two-fold cost. First, the increased model resolution results in the output of massive volumes of data, further exacerbating the “Big Data” problem. Second, the existing, general purpose software tools commonly used for analyzing, post-processing, and visualizing geoscience model outputs primarily operate on structured grid data, providing little or no support for unstructured meshes. The goal of this project, called Raijin, is to enhance the analysis and visualization tool landscape by developing community-owned, sustainable, scalable tools that facilitate operating on unstructured climate and global weather data. Working in close collaboration with atmospheric modelers the researchers plan to: 1. develop extensible, scalable, open source tools supporting fundamental analysis and visualization methods capable of operating directly (without resampling) on unstructured grid model outputs at global storm resolving resolutions; and 2. establish an active, vibrant community of user-contributors, committed to extending our work beyond the scope of this project, and helping ensure the long term sustainability of the project. The primary environment for this effort will be the Scientific Python Ecosystem. The researchers will leverage, in particular, the NSF-supported, open development Xarray and Dask packages, and the Pangeo community. To better support both of our primary goals, our work will be conducted under an open development model that encourages participation in all aspects of the project’s development.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.

用于模拟地球气候和全球天气的下一代数值模型是人类实际和准确地对地球系统建模的能力的重大进步。这些新模型解决了复杂的数学方程式，这些方程描述了地球的海洋和空间尺度上的运动，化学和能量传递，足以捕捉单个风暴。结果，这些所谓的“全球风暴解决模型”（GSRMS）产生了大量数值数据。此外，这些数据的结构比当前模型要复杂得多。 Raijin Project这项工作的目的是使用他们需要的软件工具来开发和提供全球的天气和气候建模社区，以了解这些庞大而复杂的数据集。如果没有有效探索和询问GSRM输出这些新模型的价值的能力，将受到极大的限制。 Raijin项目创建的工具将根据开源许可开发，并为世界上想要使用甚至改进的任何人免费提供它们。全球天气和气候建模社区的过渡到由非结构化的网格“动态核心”实现的公里规模的决议，带有两倍的成本。首先，增加的模型分辨率导致大量数据的输出，进一步加剧了“大数据”问题。其次，现有的通用软件工具通常用于分析，后处理和可视化地球科学模型在结构化的网格数据上输出主要操作，几乎没有或不提供对非结构化网格的支持。该项目（称为Raijin）的目的是通过开发社区拥有的，可持续的，可扩展的工具来增强分析和可视化工具格局，从而促进在非结构化的气候和全球天气数据上运行。与大气模型密切合作，研究人员计划：1。开发可扩展的，可扩展的开源工具，支持基本分析和可视化方法，能够直接（无需重新采样）在全球风暴解决方案的非结构化网格模型上进行操作（无需重新采样）；和2。建立一个活跃，充满活力的用户企业社区，致力于将我们的工作扩展到该项目的范围之外，并帮助确保项目的长期可持续性。这项工作的主要环境将是科学的Python生态系统。研究人员尤其将利用NSF支持的，开放的开发Xarray和Dask包，以及Pangeo社区。为了更好地支持我们的两个主要目标，我们的工作将在一个开放开发模型下进行，该模型鼓励参与项目开发的各个方面。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估审查标准来评估的值得支持的。