Research

Data assimilation | Computational science | Machine learning

Our research focuses on improving weather and climate predictions, better estimating the impact of events and change, and furthering NCAR efforts in community model development, and developing new computational resources.

Visualization of vapor content over North America from the CISL Visualization Gallery

Visualization of vapor content over North America from the CISL Visualization Gallery.

This 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. 

Research on the use of emerging architectures and technologies, such as GPUs and machine-learning techniques, plays a central role in these 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 our 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

We use 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, we adapt and develop 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 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 our 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), we are interested in understanding what HPC cyberinfrastructure is required for supporting AI research in the future. We’re also looking at applying machine learning to optimize the operation of HPC cyberinfrastructure itself. Another hallmark of our research portfolio is its impact on researching new algorithms and developing new tools and capabilities based on that research. AI and machine learning are increasingly important to the community’s ability to do science at scale.