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.

National Science Foundation (NSF)
Division of Advanced CyberInfrastructure (ACI)
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Sushil K Prasad
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University of Maryland Baltimore County
United States
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