Improve mathematical and computational methods for Earth System models

CISL research activities support scientific computation, numerical methods development, geophysical modeling, and the analysis of geophysical data and model experiments. These activities are chosen to lead the geophysics community in adopting new computational methods and mathematical tools to improve research.

Diverse scientific disciplines often share common tools and numerical methods. The kind of mathematical, computational, and physical sciences housed in CISL focus on areas that have broad application across scientific computation in the geosciences. Hallmarks of this research are innovative and standout contributions that not only have relevance for the overall NCAR scientific program, but also are significant in their specific area of mathematical, computational, or data science.

Guided by the NCAR strategic plan, CISL research improves predictions of weather and climate and estimations of their impacts. CISL tackles the challenges of developing new data-centric approaches, combining numerical models with observations, interpreting heterogeneous data, and quantifying the uncertainty in predictions in ways that are useful for decision making and policy. CISL also adapts scientific computing in innovative ways like accelerating computation through new algorithms and exploiting new technologies such as coprocessors. This basic computational and data science research supports NCAR’s first strategic imperative, and it also benefits the community model development central to NCAR’s Imperative 3. Finally, CISL research on the use of emerging architectures and technologies, such as graphics processors and machine learning techniques, plays a central role in NCAR’s Strategic Imperative 4, developing new computational resources.

The goal of CISL’s research activities is to sustain progress in the Earth System sciences by combining powerful supercomputing resources with the latest computational science research in algorithms, mathematical techniques, and statistical methods. CISL helps produce significant and transformative impacts on geoscience by aligning its computational resources with the research objectives of NCAR’s other laboratories and the requirements of data-centric science.

This work is supported by NSF Core funding and other sources as specified in the following sections.