Primary Convener:
Siyu Zhu, University of Oklahoma

Convener:
Yixin Wen, University of Florida
Guoqiang Tang, Wuhan University
Phu Nguyen, University of California, Irvine

Early Career Convener:
Mengye Chen, The University of Oklahoma

Chair:
Guoqiang Tang, Wuhan University
Yixin Wen, University of Florida

Machine learning (ML) methods have shown tremendous potential and advances in the understanding of Earth science. New datasets and ML models with high spatial and temporal resolutions are emerging at an unprecedented rate, which has opened up various new avenues of research in the field. This session aims to engage diverse earth scientific communities to share their novel ML methods and applications on radar & satellite observations, data fusion, Earth system modeling & forecast, natural hazard and extreme events, climate projection, environmental sustainability, explainable AI, etc. Studies within but not limited to hydrology and precipitation, with generalizable results are strongly encouraged.

Index Terms
1655 Water cycles
1854 Precipitation
1855 Remote sensing
1942 Machine learning

Co-Organized Sessions:
Global Environmental Change
Atmospheric Sciences
Natural Hazards

Suggested Itineraries:
Climate Change and Global Policy
Machine Learning and AI
Global Impacts‚ Solutions‚ & Policies

Neighborhoods:
3. Earth Covering
1. Science Nexus