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

用于模拟地球气候和全球天气的下一代数值模型代表着人类在现实和准确地模拟地球系统的能力方面的重大进步。这些新的模型解决了复杂的数学方程，这些方程描述了地球海洋和大气在空间尺度上的运动、化学和能量转移，这些空间尺度足以捕捉到单个风暴。因此，这些所谓的“全球风暴解析模式”(GSRM)产生了海量的数值数据。此外，这些数据的结构比目前的模型复杂得多。这项工作的目标是开发并向全球天气和气候建模社区提供理解这些庞大而复杂的数据集所需的软件工具。如果没有能力有效地探索和审问全球监测系统的产出，这些新模型的价值将受到极大限制。由Project Raijin创建的工具将在开放源码许可下开发，并向世界上任何想要使用甚至改进它们的人免费提供。全球天气和气候建模社区过渡到由非结构化网格“动态核心”实现的千米级分辨率，代价是双重成本。首先，模型分辨率的提高导致了海量数据的输出，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、后处理和可视化地学模型输出，主要操作在结构化网格数据上，对非结构化网格提供的支持很少或根本没有。这个名为Raijin的项目的目标是通过开发社区拥有的、可持续的、可扩展的工具来促进对非结构化气候和全球天气数据的操作，从而增强分析和可视化工具的前景。研究人员计划与大气模型人员密切合作：1.开发可扩展、可扩展、开放源码的工具，支持基本分析和可视化方法，能够以全球风暴分辨率直接(无需重采样)对非结构化网格模型输出进行操作；2.建立一个活跃、充满活力的用户贡献者社区，致力于将我们的工作扩展到本项目范围之外，并帮助确保该项目的长期可持续性。这项工作的主要环境将是Science Python生态系统。研究人员将特别利用NSF支持的开放开发X阵列和DASK包，以及Pangeo社区。为了更好地支持我们的两个主要目标，我们的工作将在开放的发展模式下进行，鼓励参与项目发展的方方面面。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.