'Machine' Learning Cloud Physics. Where Do We Stand? with Andrew Gettelman

Abstract: Cloud physics is a collection of processes that comprise the sources, evolution and sinks of water in clouds (microphysics) and their links to the dynamical environment in the atmosphere. Cloud physics is critical for both weather and climate. Important cloud processes cross many orders of magnitude in scale, and this makes them very difficult to simulate reliably from the cloud to the climate scale. Naturally, machine learning methods are providing new opportunities for advancing our understanding of clouds and how we can predict them for both weather and climate. This presentation will provide an overview and examples of how machine learning methods are being used to simulate/emulate clouds for prediction and analyze our simulations to better represent observations. The presentation will conclude with some speculation on future directions for where we might usefully apply new machine learning methods for predicting clouds and constraining uncertainties in weather and climate prediction.

Bio: Andrew Gettelman is a Senior Scientist at the Pacific Northwest National Laboratory specializing in clouds and climate change. His work concerns the development and analysis of global model simulations of the climate system with a focus on the development of physical parameterizations of clouds in global climate models and the physics and chemistry of the upper troposphere and lower stratosphere. In addition to developing and managing Earth system models, Gettelman is involved in model evaluation data analysis with satellites and field programs. Gettelman is the author or co-author on over 200 peer reviewed publications. Gettelman has a PhD in Atmospheric Sciences from the University of Washington and a BS in Civil Engineering from Princeton University.