Primary Convener:
Ching-Yao Lai, Stanford University

Convener:
Gong Cheng, Dartmouth College
Douglas Brinkerhoff, University of Montana
Mauro Perego, Sandia National Laboratories

Chair:
Gong Cheng, Dartmouth College
Douglas Brinkerhoff, University of Montana
Mansa Krishna, Dartmouth College
Yinmin Liu, Dartmouth College

Applications of machine learning tools in the cryosphere are rapidly evolving, influencing many facets of cryospheric science and transforming our ability to observe, model, and understand the Earth system. In this session, we invite submissions showcasing the use and applications of machine learning as applied to the cryosphere, including sea ice, land ice, snow, permafrost, ice sheets, ice shelves and more. We encourage submissions across a broad spectrum of applications, including - but not limited to - remote sensing, emulation of physical models, physics-informed machine learning, causal inference, calibration/initialization, uncertainty quantification, automated learning of processes, and the compilation of novel datasets. Examples of methodologies include classical statistical modelling, modern Bayesian inference frameworks, deep neural networks, or other frameworks that bring data-driven inference to bear on pressing problems in cryospheric science.

Index Terms
0758 Remote sensing
0776 Glaciology
0798 Modeling

Suggested Itineraries:
Machine Learning and AI

Neighborhoods:
3. Earth Covering