Data CI Pilot: NCAR and NEON Cyberinfrastructure Collaborations to Enable Convergence Research Linking the Atmospheric and Biological Sciences

数据 CI 试点：NCAR 和 NEON 网络基础设施合作，实现连接大气和生物科学的融合研究

• 批准号: 2039932
• 负责人: Gordon Bonan
• 金额: $ 60万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-11-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039932&HistoricalAwards=false
• 关键词: Data; CI; Pilot; NCAR; NEON

Numerical models of weather and climate such as those developed at the National Center for Atmospheric Research (NCAR) are essential research tools to advance atmospheric science. At the same time, the National Ecological Observatory Network (NEON) provides long-term, comprehensive data on the state of the terrestrial ecosystem at sites distributed across the US. Ecologists use these data to develop a predictive understanding of ecosystems at large spatial scales. NCAR models are highly complementary to NEON data products and can inform data quality, the types of data to collect, and the frequency of data collection. As these models have matured, the role of terrestrial ecosystems in climate processes has come to the forefront. To maximize the benefits to atmospheric science and ecology, this project will deliver new cyberinfrastructure tools to bring together NEON observational data products and NCAR modeling capabilities. As a result, scientific discovery and innovation will be advanced at the confluence of the geosciences and biological sciences, through more seamless and rapid integration of NCAR models and NEON observations, and the ability to provide model outputs to NEON for use by the ecological community. This project builds upon NCAR’s modeling of the land surface and its terrestrial ecosystems and NEON's observations and monitoring of terrestrial ecosystems to develop synergies between two heretofore independent research communities. We will link NCAR’s modeling capabilities in terms of the Community Earth System Model (CESM) and its Community Land Model (CLM) with NEON's measurement network through two cyberinfrastructure activities: First, we will create a portable containerized computing environment that allows university researchers to use and scientifically develop the model in conjunction with NEON data, with seamless transition across a variety of computational platforms from high performance supercomputers to laptops to cloud computing. The container image will be further enhanced by the addition of a built-in JupyterLab environment for visualization and analysis, and a collection of common notebooks that provide in-line examples and documentation of analysis methods, model configurations, and additional data transformations. The modeling system will be enhanced with automated data acquisition and preprocessing for standard configurations, enabling scientists to focus on the research rather than the technical details. Second, we will develop an automated operational analysis mode to allow for near-real time modeling of terrestrial ecosystems across the NEON network, both to verify model simulations and to supplement NEON measurements with model output, as well as validate NEON input data. We will use a cloud-based version of CESM/CLM in conjunction with cyberinfrastructure that monitors NEON outputs to spin up a configurable set of analysis runs whenever new data are available. The operational analysis will include site-specific runs focused on NEON measurement locations, giving regular feedback to modeling and observational teams, improving the quality of both. The ability to ingest near real time data is a novel capability, in contrast with standard datasets for model evaluation that are typically several years old.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.

天气和气候的数值模型，例如在国家大气研究中心（NCAR）开发的天气和气候模型是推进大气科学的重要研究工具。同时，国家生态天文台网络（NEON）提供了有关在美国分布的地点的陆地生态系统状态的长期，全面的数据。生态学家使用这些数据来对大空间尺度上的生态系统进行预测理解。 NCAR模型对霓虹灯数据产品是高度完善的，可以为数据质量，收集数据的类型以及数据收集的频率提供信息。随着这些模型的成熟，陆地生态系统在气候过程中的作用已成为最前沿。为了最大程度地利用大气科学和生态学的好处，该项目将提供新的网络基础设施工具，以将霓虹灯观察数据产品和NCAR建模能力融合在一起。结果，科学发现和创新将在地球科学和生物科学的融合中提出，通过对NCAR模型和霓虹灯观察结果的更无缝和快速整合，以及向生态群落提供霓虹灯的模型输出的能力。该项目建立在NCAR对土地表面及其陆地生态系统以及霓虹灯的观察结果和监测陆地生态系统的建模的基础上，以在两个迄今独立研究社区之间发展协同作用。我们将通过两个网络基础设施活动来将NCAR的建模能力与霓虹灯的测量网络联系起来，将Neon的测量网络与霓虹灯的测量网络联系起来：首先，我们将建立一种便携式的集装箱计算环境，使大学研究人员能够与Neon数据相结合，与NEON的跨度范围相结合，使大学研究人员与Neon的模型相结合，并将其与NEON的模型进行科学开发，并与NEON的范围进行综合的综合量表，以综合量度的计算量，以综合量的计算量，综合综合综合综合的计算机，以综合综合的综合综合量，计算。通过添加内置Jupyterlab环境进行可视化和分析，将进一步增强容器图像，以及提供分析方法，模型配置和其他数据转换的常见笔记本电脑集合。通过自动数据采集和预处理标准配置的预处理，将增强建模系统，使科学家能够专注于研究而不是技术细节。其次，我们将开发一种自动化的操作分析模式，以允许对整个霓虹灯网络的陆地生态系统进行近实的时间建模，以验证模型模拟和用模型输出补充霓虹灯测量以及验证霓虹灯输入数据。我们将使用基于云的CESM/CLM版本与网络基础结构结合使用，该版本在可用的新数据可用时监视霓虹灯输出来旋转一组可配置的分析集。运营分析将包括针对霓虹灯测量位置的特定地点运行，对建模和观察团队进行定期反馈，从而提高两者的质量。与标准数据集相比，近乎实时数据的能力是一种新颖的功能，与用于模型评估的标准数据集相反。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来审查标准的评估值得支持的。