CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources

该项目将开发一个培训计划，用于交叉培训三个学科（计算，数学和大气科学）的参与者，包括研究生，博士后以及初级教师，以促进使用先进的网络基础设施（CI）资源和技术的多学科研究和教育。 培训重点将是大气科学主题，需要高性能计算（HPC）和大数据的知识和技能。 这个“数据+计算+大气科学”培训计划的影响包括：1）为高级CI准备更好的科学工作者; 2）扩大CI的采用和全国范围内招募的学员的可及性; 3）通过多学科研究培训和团队项目补充课程设置。 因此，该项目服务于国家利益，正如NSF的使命所述，通过促进科学进步和促进国家繁荣和福利。培训计划将侧重于大气辐射收支的建模和分析。 云在地球气候系统中起着重要作用，特别是其辐射能量收支。 全球气候数值模式（GCM）云模拟技术的新进展通常伴随着较高的计算成本，这使得高性能计算成为不可或缺的工具。 卫星遥感技术的发展使我们观测大气状况和评估GCM的方式发生了重大变化，并导致了越来越多的大型数据集。 由于这些进步和变化，HPC和大数据比以往任何时候都更加成为解决大气科学中一些最具挑战性问题的基本知识和技能的一部分。 该项目将在广泛的层面上对参与者进行交叉培训，包括研究生，博士后以及来自计算，应用数学和大气科学领域的初级教师。 该项目的目标是利用先进的竞争与信息资源和技术促进多学科劳动力的发展和合作。 该培训计划包括1）针对三个学科的共性和差异进行定制课程设计; 2）在HPC，大数据，应用数学和大气科学中选择主题的在线教学; 3）教师协助的团队研究项目。

This project will develop a training program for cross-training of participants, including graduate students, postdocs, as well as junior faculty, from three disciplines (Computing, Mathematics, and Atmospheric Sciences) to foster multidisciplinary research and education using advanced cyberinfrastructure (CI) resources and techniques. The training focus will be Atmospheric Sciences topics that require knowledge and skills of high performance computing (HPC) and Big Data. The impacts of this "Data + Computing + Atmospheric Sciences" training program include 1) prepare a better scientific workforce for advanced CI; 2) broaden CI adoption and accessibility for trainees recruited nationwide; 3) complement curricular offerings through multidisciplinary research training and team-based projects. The project, thus, serves the national interest, as stated by NSF's mission, by promoting the progress of science and advancing the national prosperity and welfare.The training program will focus on modeling and analysis of atmospheric radiation budget. Clouds play an important role in Earth's climate system, particularly its radiative energy budget. New technical advances of cloud simulation in numerical global climate models (GCM) usually come with high computational cost, which makes HPC an indispensable tool. The advances of satellite-based remote sensing techniques have made a significant change in our way to observe the state of the atmosphere and evaluate GCM, and have led to growing amount of large datasets. Because of these advances and changes, more than ever, HPC and Big Data have become parts of essential knowledge and skills to tackle some of the most challenging questions in Atmospheric Sciences. The project will conduct cross-training of participants on a wide range of levels, including graduate students, postdocs, as well as junior faculty from the areas of Computing, Applied Mathematics, and Atmospheric Sciences. The goal of the project is to foster multidisciplinary workforce development and collaboration using advanced CI resources and techniques. The training program includes 1) customized course design for three disciplines with commonalities and differences; 2) online instruction on selected topics in HPC, Big Data, Applied Mathematics, and Atmospheric Sciences; 3) faculty-assisted team-based research projects.