Columbia Climate School’s Office of Faculty Affairs is launching a new Faculty Seminar Series. We will be featuring talks from internal and external speakers on interdisciplinary research related to climate and sustainability. We are pleased to announce that our first Climate School Faculty Seminar will be by Dr. Kara Lamb, titled "Learning Cloud Processes across Scales using Scientific Machine Learning" on Wednesday, September 11, from 2 - 3:30pm in the Forum Room 301. The talk abstract and the speaker’s bio are below. We'd appreciate you joining us for this event. Please register here to join us for the event
If joining on Zoom, please RSVP here: https://forms.gle/uJVmy6UpDCUMxTjp7
Title: Learning Cloud Processes across Scales using Scientific Machine Learning
Abstract: Clouds remain one of the greatest sources of uncertainty in predicting future climate, as they involve complex, non-linear processes that extend from the submicron scale to the kilometer scale. Our current ability to model clouds is limited by significant uncertainties, particularly in the intricate microphysical processes that govern the interaction and growth of cloud droplets and ice crystals, as well as in accurately modeling clouds across the relevant temporal and spatial scales for climate. Recent advances in scientific machine learning offer promising methods to address these challenges. I will discuss several recent studies applying these methods to cloud processes. First, I will discuss how physics-informed machine learning and recently developed equation discovery methods can be used to reduce structural uncertainty in models of ice growth in the atmosphere using in situ observations from laboratory experiments and airborne field campaigns. Second, I will discuss how data-driven reduced order modeling can be used to develop simplified (bulk) microphysics schemes in an unsupervised manner from more detailed microphysical models. Finally, I will discuss how these methods can be used to learn relevant information from high resolution global storm resolving models, to improve the prediction of precipitation extremes by representing cloud processes at the spatial scales needed to accurately predict processes at the climate scale.
Bio: Dr. Kara Lamb is an Associate Research Scientist in the Department of Earth and Environmental Engineering and in the Learning the Earth with Artificial Intelligence and Physics (LEAP) Center at Columbia University. She studies physical climate processes in the context of natural and anthropogenic perturbations to the climate system. Her research lies at the intersection of observations (from laboratory and airborne field studies) and high-resolution modeling, combining traditional process-based approaches with data science and machine learning. A focal point of her research is how aerosols and clouds impact climate and air pollution, and how machine learning can be used to inform climate mitigation strategies related to these systems. She currently leads projects funded by the DOE and the Zegar Family Foundation, and collaborates with researchers at NASA GISS. She received her PhD in physics from the University of Chicago, and worked as a research scientist at CIRES/NOAA, where she was on the science team for the NASA KORUS-AQ and ATOM aircraft campaigns and the NOAA FIREX Firelab study.
