Research in computational science and math for geophysics: TDD and IMAGe
Nonlinear structures in a magnetohydrodynamic simulation: these two figures visualize the current in a simulation of the flow of a charged fluid. The sheets are being forced together based on magnetic pressure. Also of interest is that the magnetic field lines associated with each sheet are not aligned. Creating this interaction among coherent structures is possible due to the high resolution of this simulation that allows for the thinning of current sheets. An analagous feature has been observed in the Earth's magentosphere, and it is important that this effect can be captured using a direct numerical simulation but with less complex physical processes. The research activities within CISL's Technology Development Division (TDD) and Institute for Mathematics Applied to Geosciences (IMAGe) support scientific computation, numerical methods, geophysical modeling, and the analysis of geophysical data and model experiments. This research is important to maintain an innovative computational and modeling facility at NCAR, and more broadly, to lead the geophysics community in adopting new computational methods and mathematical tools that enhance scientific research. This mission is aligned with NCAR's strategic priority of "Conducting computer science, computational science, applied mathematics, statistics, and numerical methods R&D."
Given this broad priority, the research in CISL must span several disciplines and address computational science at many levels. These range from improvements to network flow and scalability of existing codes to simulating the flow of geophysical fluids using high performance computational platforms. Integrated with the computational science are areas of applied mathematics that include data analysis, models for multiscale processes, and techniques for assimilating data into numerical models. Because these different elements are coordinated through a single lab, there is an easy transfer of technology and ideas from prototypes and theoretical results in IMAGe, to issues of implementation, efficiency, and workflow in TDD, and finally incorporation as tools and models for the communities served by CISL. There is also a valuable reverse transfer whereby emerging computational capability and data storage spur particular research that takes advantage of these features.
Support by funding agencies other than the NSF is indicated in the individual reports in this section of the CISL annual report.