Creation of novel, scalable software algorithms to meet the needs of climate science.
Integration of high resolution global climate models has become feasible on the current generation of DOE supercomputers. One of the principal motivations for these models are their superior representation of storms and extreme weather. Fine spatial grids are needed to capture these processes, as well as high frequency temporal output. Cumulatively, this has the effect of dramatically increasing the size of datasets that need to be processed by analysis software. Traditional serial analysis tools and methods are incapable of handling multi-terabyte datasets.
We are developing a number of tools that are capable of addressing these contemporary challenges. The codes are written in C++ and R and are capable of running on current desktop workstations as well as the largest DOE HPC platforms (NERSC, ALCF, etc). The tools follow best scientific data management practices and primarily utilize MPI for parallel execution on contemporary distributed memory, multi-core hardware.
- TECA: A Parallel Toolkit for Extreme Climate Analysis (TECA)
- R package
- Python package