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.

Software Tools:


Expertise is provided by the computation and predictions team to streamline existing capabilities, scalable algorithms, and deployment of software.

A major task for the visualization and analysis team is to provide support and enable the delivery of the software stack and the analysis routines developed during the research and development cycle of the CASCADE SFA (Scientific Focus Area). The team is deploying on several leadership computing facilities such as NERSC  and ANL as well as provide installations that support local Unix and Mac clients.


  • Python (mpi4py- MPI support, numpy – data access, rpy2 – Bridge to R, UV-CDAT – Climate science analysis tools)
  • R – (llex – stats packages, extremes – stats, pbdR/pbdMPI – MPI)
  • A Parallel Toolkit for Extreme Climate Analysis – TECA – (Map/Reduce C++)
  • One-on-one software support for all CASCADE teams
  • Support for debugging and execution of runs to meet publication deadlines