The Computational Science Center (CSC) is a multidisciplinary research group with research initiatives focusing on high-performance, parallel, cluster, grid and cloud computing systems, emerging parallel architectures, and scientific application development. In addition, the CSC manages the University of Colorado’s Department of Computer Science’s supercomputing facility.
If you are new to the CSC, the links below will help you with operating the systems and software provided by the center. If you have any questions, feel free to email firstname.lastname@example.org.
Additional documentation for other systems can be found in the Systems section.
NCAR researchers, funded in part by the Department of Energy’s Climate Change Prediction Program, have built a scalable and efficient spectral-element-based atmospheric dynamical core using the Computational Science Section’s High Order Method Modeling Environment (HOMME). In order for this to be a useful tool for atmospheric scientists it is necessary to couple this core to physics packages employed by the community.
The physics of cloud formation is generally simulated rather crudely using phenomenological parameterizations. The dream of modelers is the direct numerical simulation of cloud processes on a global scale. Unfortunately, this requires an increase in computational power of approximately six orders of magnitude over what is currently available. A promising alternative to improve the simulation of cloud processes in climate models is a compromise technique called Cloud Resolving Convective Parameterization (CRCP, also known as Super-Parameterization). The cost of this approach is two to three orders of magnitude more expensive than traditional parameteriz ation techniques. However, with the advent of BlueGene/L this is now tractable. We have built a super-parameterization package and work is underway to couple this to HOMME. The result will be an atmospheric model capable of exploiting BG/L’s scalability and computational power to realize practical and scientifically useful integration rates for super-parameterized climate simulation.