JSM2023
Back to the program
Topic-Contributed Paper Session

Deep learning for climate change: forecasts, mitigation, and adaption

Tue, Aug 8, 2:00 PM
Trevor HarrisOrganizerReetam MajumderChair
Section on Statistics and the Environment

5 Presentations

An autotuning approach to DOE's earth system model using machine learning
2:05 PM - 2:25 PM
Lyndsay Shand (Sandia National Laboratories)
Characterizing climate pathways using feature importance on echo state networks
2:25 PM - 2:45 PM
Katherine Goode (Sandia National Laboratories)
Emulating complex climate models via integrating variational autoencoder and spatial extremes
2:45 PM - 3:05 PM
Likun Zhang (University of Missouri-Columbia)
Co-authors: Xiaoyuan Ma, Christopher Wikle (University of Missouri-Columbia), Raphael Huser (KAUST)
Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process
3:05 PM - 3:25 PM
Brian Reich (North Carolina State University)
Co-authors: Reetam Majumder (North Carolina State University), Benjamin Shaby (Colorado State University)
Spatial uncertainty quantification for deep climate model integration
3:25 PM - 3:45 PM
Trevor Harris
↑