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

该项目将制定一个培训计划，对来自三个学科(计算、数学和大气科学)的参与者进行交叉培训，包括研究生、博士后和初级教师，以利用先进的网络基础设施(CI)资源和技术促进多学科研究和教育。培训重点将是大气科学主题，这些主题需要高性能计算(HPC)和大数据的知识和技能。这一“数据+计算+大气科学”培训计划的影响包括1)为先进的CI培养更好的科学队伍；2)扩大CI的采用率和可及性；3)通过多学科研究培训和基于团队的项目来补充课程设置。因此，正如NSF的使命所述，该项目服务于国家利益，促进科学进步，促进国家繁荣和福祉。培训计划将重点放在大气辐射预算的建模和分析上。云在地球气候系统中扮演着重要的角色，特别是在其辐射能量收支中。全球数值气候模式(GCM)中云模拟的新技术进展往往伴随着较高的计算代价，这使得HPC成为不可缺少的工具。卫星遥感技术的进步使我们观测大气状态和评估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成为解决大气科学中一些最具挑战性的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传播与信息资源和技术促进多学科的劳动力发展和协作。培训计划包括1)针对三个具有共性和差异的学科进行定制的课程设计；2)针对HPC、大数据、应用数学和大气科学的选定主题进行在线教学；3)教员协助的基于团队的研究项目。

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.