STAMPS@CMU presents:

Machine Learning Emulation across the Earth System

by David John Gagne (National Center for Atmospheric Research)

Online webinar January 22, 2021 at 1:30-2:30 PM ET.
For connection information, join our mailing list here.

Abstract
Earth system processes can be explicitly modeled to a high degree of complexity and realism. The most complex models also are the most computationally expensive, so in practice they are not used within large weather and climate simulations. Machine learning emulation of these complex models promises to approximate the complex model output at a small fraction of the original computational cost. If the performance is satisfactory, then the computational budget could be steered toward other priorities. The NCAR Analytics and Integrative Machine Learning group is currently working on machine learning emulation problems for microphysics, atmospheric chemistry, and processing holographic observations of rain drops. We will discuss our successes as well as challenges in ensuring robust online performance and incorporating emulators within existing simulations.

Bio

David John Gagne is a Machine Learning Scientist and head of the Analytics and Integrative Machine Learning group at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. His research focuses on developing machine learning systems to improve the prediction and understanding of high impact weather and to enhance weather and climate models. He received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an Advanced Study Program postdoctoral fellowship at NCAR in 2018.
He has collaborated with interdisciplinary teams to produce machine learning systems for hail, tornadoes, hurricanes, and renewable energy. In order to educate atmospheric science students and scientists about machine learning, he has led a series of interactive short courses and hackathons.