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

国家大气研究中心(NCAR)开发的天气和气候数值模型是推动大气科学发展的重要研究工具。与此同时，美国国家生态观测站网络(NEON)提供分布在美国各地的陆地生态系统状况的长期、全面的数据。生态学家使用这些数据来发展对大空间尺度上的生态系统的预测性理解。NCAR模型是对霓虹灯数据产品的高度补充，可以告知数据质量、要收集的数据类型以及数据收集的频率。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经凸现出来。为了最大限度地提高大气科学和生态学的效益，该项目将提供新的网络基础设施工具，将霓虹灯观测数据产品和NCAR建模能力结合在一起。因此，通过更无缝和快速地整合NCAR模型和霓虹灯观测，以及向霓虹灯提供模型产出以供生态界使用的能力，将在地球科学和生物科学的交汇处促进科学发现和创新。该项目建立在NCAR对陆地表面及其陆地生态系统的模拟以及霓虹灯对陆地生态系统的观测和监测的基础上，以发展两个迄今独立的研究界之间的协同作用。我们将通过两项网络基础设施活动，将NCAR在社区地球系统模型(CESM)及其社区陆地模型(CLM)方面的建模能力与霓虹灯的测量网络联系起来：首先，我们将创建一个可移植的集装箱化计算环境，允许大学研究人员结合霓虹灯数据使用和科学开发模型，实现从高性能超级计算机到笔记本电脑再到云计算的各种计算平台的无缝过渡。容器图像将通过添加用于可视化和分析的内置JupyterLab环境以及提供分析方法、模型配置和其他数据转换的内嵌示例和文档的常见笔记本集合来进一步增强。建模系统将通过自动数据采集和标准配置的预处理来增强，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业务分析模式，以允许对整个霓虹灯网络中的陆地生态系统进行近乎实时的建模，以验证模型模拟，并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，并结合监控霓虹灯输出的网络基础设施，以便在有新数据时启动一组可配置的分析运行。业务分析将包括侧重于霓虹灯测量地点的特定地点运行，定期向建模和观测团队提供反馈，提高两者的质量。与通常已有数年历史的模型评估标准数据集相比，获取近实时数据的能力是一种新的能力。这一奖项反映了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。

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.