CISL research

Overview | Data assimilation | Computational science | Machine learning | Collaboration

Visualization image from a regional climate simulation.


The overall goal of CISL’s research activities is to sustain progress in Earth system science through both fundamental and applied data science and computational science research. Our research focuses on improving predictions of weather and climate and better estimating the impact of events and changes; furthering strategic NCAR efforts in community model development; and developing new computational resources. CISL research on the use of emerging architectures and technologies, such as GPUs and machine-learning techniques, plays a central role in those efforts. While our research is aimed at supporting NCAR’s science mission, it also has broad applicability across the geosciences and beyond.

Data assimilation

Among the key topics in CISL’s research portfolio are developing novel data assimilation capabilities, advancing the production and compression of climate and weather data, and refining the state of the art in advanced visualization of scientific data. A prime example is the Data Assimilation Research Testbed (DART), a community framework for ensemble data assimilation research and applications. In addition to developing advanced methods for data assimilation, we collaborate with modelers and observationalists to develop data assimilation capabilities for new models and observations.

Computational science

CISL does applied computational science research in high-performance computing (HPC) to understand how to create highly scalable, portable applications for emerging exascale computing architectures. For example, CISL adapts and develops computational science methods and tools to accelerate the pace of optimizing and porting code to run on CPU-GPU architectures and GPUs, enabling NCAR applications to exploit new technologies.

Our research contributes in a number of other important ways to improving weather and climate predictions and better estimating the regional and local impact of weather events and climate trends. Results come from our ongoing work to advance the development of Earth system models, downscaling methods, scientific workflows for large data sets, and the needs and constraints of regional and local stakeholders. This effort integrates CISL expertise in data science and impact assessment with the goal of transferring climate science into useful products for decision making in adaptation research and risk analysis.

Machine learning

In the rapidly advancing area of artificial intelligence (AI), CISL is interested in understanding what HPC cyberinfrastructure is required for supporting AI research in the future, and also in applying machine learning to optimize the operation of HPC cyberinfrastructure itself. Another hallmark of CISL’s research portfolio is its impact on researching new algorithms and developing new tools and capabilities based on that research. Artificial intelligence and machine learning are increasingly important to the community’s ability to do science at scale.


Another hallmark of CISL research is collaboration. Our staff has collaborated with UCAR-member universities in a variety of data assimilation projects involving literally dozens of models. We also have established collaborations with all of the other NCAR laboratories and routinely engage groups at major institutions such as the U.S. Department of Energy, National Oceanic and Atmospheric Administration, other national laboratories, universities, the private sector, and international institutions.

Finally, CISL’s research is enhanced by a robust set of ongoing partnerships and community engagement activities. These partnerships take the form of joint appointments with other NCAR labs; university partnerships focused on HPC workforce development; research and development projects with public and private sectors; and recurring conferences, workshops, and training events that focus on fostering the understanding and effective use of emerging technologies and techniques within the community.