Cyberinfrastructure for Sustained Scientific Innovation - Software Elements: Cloud WRF for the Atmospheric Research and Education Communities

该合同支持在云环境中建立天气与研究预报（WRF）模型的官方支持版本。WRF是世界上最流行的数值天气预报模式，由美国国家大气研究中心（NCAR）支持，面向大学、研究实验室和业务气象中心的用户社区。该项目将处理一些基本问题，例如建模系统的可访问性、对研究和教育用户社区的模型支持的改进、学生和科学家的培训，以及模型开发的促进。鉴于WRF在大气研究和实时天气预报方面的突出地位，这项工作不仅将促进科学的进步，而且还将有助于国家网络基础设施（CI）资产之一的开发和应用活力，这是一种用于业务中心和公共目的的关键天气预报模型。此外，该项目将有助于少数民族机构的教育，并将提供更好地培训新一代国家大气科学家的工具。最后，该项目将利用商业云服务提供商的资源和支持，在主要研究人员与工业界的合作下，为这些国家利益服务。在云中运行WRF的可行性之前已经得到了证明，该项目将通过在云中配置和支持WRF的官方版本来推进该领域，并将WRF系统代码的最新云配置版本同步到WRF GitHub存储库。其他可用的材料包括WRF教程材料和系统文档、WRF输入和输出数据集以及WRF测试框架代码分析包。该项目将产生一个配置和文档化的云WRF系统，该系统将为模型使用开辟一个新的领域，并将提高用户支持和集成所贡献模型改进的效率。这种云功能将利用新兴的公共网络基础来促进目标模型的代码开发，并允许在代码分析和测试中实现关键的再现性。它将通过建立全球可访问的建模空间，增强指令的可移植性和访问，降低主机机构的成本，以及提高在线材料的可用性，推进建模系统教程的交付，从而促进科学家的培训和教育。这些新的云功能将解决模型支持和开发中的瓶颈，并且一旦开发完成，CI可以用于其他社区模型，从而通过重用使其他科学学科及其工具受益。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会地球科学理事会的交叉项目共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports the establishment of an officially-supported version of the Weather and Research Forecast (WRF) model in the cloud environment. WRF is the world's most popular numerical weather prediction model and is supported by the National Center for Atmospheric Research (NCAR) for a community of users across universities, research labs, and operational weather centers. This project will address fundamental issues such as modeling system accessibility, improvement of model support to the research and educational user communities, student and scientist training, and facilitation of model development. Given WRF's prominence in both atmospheric research and real-time weather forecasting, this work will not only promote the advancement of science, but also will contribute to the vigor of development and application of one of the nation's cyberinfrastucture (CI) assets, a key weather prediction model used at operational centers and for public purposes. Furthermore, this project will contribute to education at minority-serving institutions and will yield tools to better the training of new generations of the nation's atmospheric scientists. Lastly, the project will leverage the resources and support of commercial cloud service providers to serve these national interests, in a collaboration of the principal researchers with industry.The viability of running the WRF in the cloud has been previously demonstrated, and this project would advance the field by configuring and supporting the official version of the WRF in the cloud, with an up-to-date, cloud-configured version of the WRF system code synced to the WRF GitHub repository. Other materials that will be available include the WRF tutorial materials and system documentation, WRF input and output datasets, and the WRF Testing Framework code analysis package. The project will produce a configured and documented cloud WRF system that will open a new arena for model use and that will enhance the efficiency of both user support and the integration of contributed model improvements. This cloud capability will exploit an emerging common cyber-ground to facilitate code development for the target model and allow for critical reproducibility in code analysis and testing. It will advance the delivery of modeling system tutorials, and thus scientist training and education, through establishing globally-accessible modeling spaces, enhancing instruction portability and access, decreasing costs for host institutions, and improving usability of on-line materials. These new cloud capabilities will be addressing bottlenecks in model support and development, and once developed, the CI can be adapted for other community models, thus benefiting other scientific disciplines and their tools via its reuse.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Program within the NSF Directorate for Geosciences.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.