Primary Convener:
Jianfeng Li, Pacific Northwest National Laboratory

Convener:
Samiah Moustafa, Bay Area Environmental Research Institute Moffett Field
Dasa Gu, The Hong Kong University of Science and Technology
Ruben Delgado, Hampton University
Guanyu Huang, Spelman College
Aoxing Zhang, Southern University of Science and Technology
Yuzhong Zhang, Westlake University

Early Career Convener:
Ziming Chen, Pacific Northwest National Laboratory

Chair:
Dasa Gu, Pacific Northwest National Laboratory
Yuzhong Zhang, Westlake University
Guanyu Huang, Spelman College
Jianfeng Li, Georgia Institute of Technology

Artificial Intelligence (AI), particularly machine learning and deep learning, is catalyzing transformative advances in Earth and Atmospheric Sciences. By harnessing vast datasets, AI offers innovative solutions that enhance our comprehension of complex processes.
This session invites contributions exploring AI applications across atmospheric, terrestrial, riverine, oceanic, and Earth-related research domains. Topics may include: remote sensing, weather/air quality/sea-level forecasting, extreme events/natural hazards, super-resolution and downscaling, land use/land cover change, crop prediction, water quality/coastal water monitoring, and Earth system modeling. We also welcome strategies for integrating AI into education and research training, preparing students for data-intensive, interdisciplinary careers. Special emphasis will be placed on initiatives that expand access to AI tools and training, promote open science and responsible AI practices, and foster collaboration across research, education, and industry. By incorporating insights from diverse fields, we aim to broaden perspectives on AI-driven advancements in Earth and atmospheric sciences and their transformative impact on education.

Index Terms
3315 Data assimilation
0555 Neural networks, fuzzy logic, machine learning
1610 Atmosphere
1622 Earth system modeling

Cross-Listed:
NH - Natural Hazards
SY - Science and Society
ED - Education
GC - Global Environmental Change

Suggested Itineraries:
Machine Learning and AI

Neighborhoods:
3. Earth Covering