Seminars + Panels

Fall 2022

Fall Lecture: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics
Speaker: Dr. David Rolnick
Date: Thursday, September 8
Time: 3:00 p.m.–4:30 p.m. EST
Format: Hybrid (in-person and virtual options)
In-person location: Columbia Innovation Hub, Tang Family Hall (Room 202), 2276 12th Avenue, New York, NY 10027 (Directions)
Virtual option: YouTube link

Abstract: Machine learning (ML) can be a powerful tool in helping society reduce greenhouse gas emissions and adapt to a changing climate. In this talk, we will explore opportunities and challenges in ML for climate action, from optimizing electrical grids to monitoring crop yield and biodiversity, with an emphasis on how to incorporate domain-specific knowledge into machine learning algorithms. We will also consider ways that ML is used in ways that contribute to climate change, and how to better align the use of ML overall with climate goals.

Bio: David Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila Quebec AI Institute. He is a Co-founder and Chair of Climate Change AI and serves as Scientific Co-director of Sustainability in the Digital Age. Dr. Rolnick received his Ph.D. in Applied Mathematics from MIT. He is a former NSF Mathematical Sciences Postdoctoral Research Fellow, NSF Graduate Research Fellow, and Fulbright Scholar, and was named to the MIT Technology Review’s 2021 list of “35 Innovators Under 35.”

Summer 2022

Seminar: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics
Speaker: Dr. Hugh Morrison
Date: Thursday, July 21
Time: 11:00 a.m.–12:30 p.m. EST
Format: Hybrid (in-person and virtual options)
In-person location: Columbia Innovation Hub, Tang Family Hall (Room 202), 2276 12th Avenue, New York, NY 10027 (Directions)
Virtual option: Zoom link

Abstract: In the atmosphere, microphysics – the small-scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting – is a critical part of Earth’s climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent particle populations statistically using microphysics parameterization schemes. However, there are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and fundamental knowledge gaps in microphysical processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations. I will discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented within microphysics schemes, a “particle-based” approach is advocated that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing critical gaps in knowledge of microphysical processes. I will also advocate using statistical modeling tools to improve how these observations are used to constrain microphysics schemes. Finally, a hierarchical approach will be outlined that combines the various pieces discussed in this talk, providing a possible blueprint for accelerating progress in how microphysics is represented in models.

Bio: Hugh Morrison studies cloud physics and dynamics, especially using numerical models. He grew up in Minnesota and got his B.S. at the University of Minnesota in 1997. He moved to Boulder, Colorado and received his M.S. in 2000 and Ph.D. in 2003 at the University of Colorado in Atmospheric Science. He then went to the National Center for Atmospheric Research (NCAR) as a post-doc in the Advanced Study Program. Hugh became a staff scientist at NCAR in 2008 and is currently a senior scientist in the Mesoscale and Microscale Meteorology Laboratory at NCAR.

Bayesian Modeling for Climate Modeling
Speaker: Dr. Marcus van Lier-Walqui
Date: Friday, June 24, 2022
Time: 2:00pm – 4:00pm EST
Format:Hybrid (in-person and virtual options)
In-person location: Columbia Innovation Hub, Tang Family Hall (Room 202), 2276 12th Avenue, New York, NY 10027 (Directions)
Virtual option: Zoom link

Abstract: In the atmosphere, microphysics – the small-scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting – is a critical part of Earth’s climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent particle populations statistically using microphysics parameterization schemes. However, there are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and fundamental knowledge gaps in microphysical processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations. I will discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented within microphysics schemes, a “particle-based” approach is advocated that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing critical gaps in knowledge of microphysical processes. I will also advocate using statistical modeling tools to improve how these observations are used to constrain microphysics schemes. Finally, a hierarchical approach will be outlined that combines the various pieces discussed in this talk, providing a possible blueprint for accelerating progress in how microphysics is represented in models.

Spring 2022

Seminar: Machine Learning Rain to Improve Weather and Climate Prediction
Speaker: Dr. Andrew Gettelman, National Center for Atmospheric Research (NCAR)
Date: Wednesday, April 27, 2022
Time: 4:30pm – 5:30pm EST
Format:Hybrid (in-person and virtual options)
In-person location: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 206, New York, NY 10027 (Directions)
Virtual option: Zoom link provided upon registration

Abstract: Rain is a critical part of the hydrologic cycle and a key process in understanding the evolution of climate. This talk will provide a background on why and how rain is a critical part of the climate system. The methods for representing rain formation at the scale of micrometer (10-6m) in models which represent the earth’s general circulation (at the scale of 10+6m) are approximate, and this is a critical problem in weather and climate. To approach this problem, we take detailed numerical approaches to the rain process typically used for small scale models, and put them in a global model. This results in simulations that have higher fidelity with respect to observations and reduce longstanding biases in the global model, but at prohibitive computational cost. We then train neural networks on the detailed treatments, and put those back into the full model. This reproduces all the major features of the detailed simulations from weather to climate, with no impact in computational cost. Diagnostics on the neural networks illustrate the challenges in representing these processes with machine learning such as the potential for overfitting and problems with extrapolation, as well as how physical processes important for climate challenge machine learning methods and provide opportunities for extending them.

Bio: Dr. Andrew Gettelman is a senior scientist at the National Center for Atmospheric Research, specializing in global climate modeling. His work concerns development and analysis of global model simulations of the climate system with a focus on 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 community earth system models at NCAR, Dr. Gettelman is involved in model evaluation data analysis with satellites and field programs, having served on several satellite science teams and on the science teams for field projects. Dr. Gettelman has been a visiting professor in Physics in Oxford, an Erskine visiting fellow at the University of Canterbury in Christchurch, New Zealand, and a visiting professor in the Institute for Atmospheric and Climate Science at the Swiss Federal Institute of Technology (ETH) in Zürich, Switzerland. Dr. Gettelman is a member of the atmospheric model development team for the Community Earth System Model. Dr. Gettelman is a contributing author and reviewer for international scientific assessments of climate change and ozone depletion. He has served on the COMET advisory board, and the NCAR Earth Observing Laboratory advisory board. Dr. Gettelman is author or co-author on over 200 peer reviewed publications, and a textbook (Demystifying Climate Models, Springer). Dr. Gettelman has a PhD in Atmospheric Sciences from the University of Washington and a Bachelors of Science in Civil Engineering from Princeton University.

Panel: LEAP, Learning the Earth with Artificial Intelligence and Physics (video recording)

LEAP Team Presenters: Courtney D. Cogburn, Pierre Gentine, and Tian Zheng, Columbia; Dave Lawrence, National Center for Atmospheric Research (NCAR)
Date: Tuesday, April 26, 2022
Time: 2:00pm – 3:00pm ET
Format: Hybrid (in-person and virtual options)
In-person location: Columbia Business School, Kravis (Room 690), 665 W 130th Street, New York, NY 10027
Virtual option: Zoom link provided upon registration 

Please join the Computational Social Science (CSS) Working Group for a special event with LEAP, Learning the Earth with Artificial Intelligence and Physics, the recently launched NSF STC here at Columbia University. We will discuss the core areas of LEAP’s research, why education, equity, and bi-directional knowledge transfer are core to LEAP’s mission, why climate modeling may be important for your scholarship, and the open-access data and computing platform, LEAPangeo, that we hope will transform climate data science. In addition to introducing LEAP to the broader Columbia community, our discussion will also set a foundation for LEAP engagement, collaboration, and data and knowledge sharing generally.

For speaker bios and more information, please view the event listing, courtesy of Columbia’s Data Science Institute.

Seminar: From probabilistic forecasting to neural data compression and back: a latent variable perspective.

Speaker: Stephan Mandt, UC Irvine
Date: Monday, March 28, 2022
Time: 4:10pm – 5:10pm ET
Format: Hybrid (in-person and virtual options)
In-person location: School of Social Work, 1255 Amsterdam Avenue, Room 903 (Directions)
Virtual option: Zoom Link (passcode 860249)

Abstract: The past few years have seen deep generative models mature into promising applications. Two of these applications include neural data compression and forecasting high-dimensional time series, including video. I will begin by reviewing the basic ideas behind neural data compression and show how advances in approximate Bayesian inference and generative modeling can significantly improve the compression performance of existing models. Finally, I show how neural video codecs can inspire probabilistic forecasting, leading to probabilistic sequence prediction methods with high potential for data-driven weather prediction.

Bio: Stephan Mandt is an Assistant Professor of Computer Science and Statistics at the University of California, Irvine. From 2016 until 2018, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research, first in Pittsburgh and later in Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne, where he received the German National Merit Scholarship. Furthermore, he is a Kavli Fellow of the U.S. National Academy of Sciences, an NSF CAREER Awardee, a member of the ELLIS Society, and a former visiting researcher at Google Brain. Stephan regularly serves as an Area Chair, Action Editor, or Editorial Board member for NeurIPS, ICML, AAAI, ICLR, TMLR, and JMLR. His research is currently supported by NSF, DARPA, DOE, Disney, Intel, and Qualcomm.

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