Primary Convener:
Mingyue Tang, University of Hawai'i at Mānoa

Convener:
Sandro Lubis, Pacific Northwest National Laboratory
Jing-Jia Luo, Bureau of Meteorology
X. Li, CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Center for Ocean Mega-Science, Chinese Academy of Sciences
Mingting Li, Sun Yat-sen University
Yoo-Geun Ham, Seoul National University
Swadhin K. Behera, JAMSTEC
Ning Zhao, JAMESTEC

Early Career Convener:
Mingyue Tang, University of Hawai'i at Mānoa

Chair:
Mingyue Tang, University of Hawai'i at Mānoa
Sandro Lubis, Pacific Northwest National Laboratory
Jing-Jia Luo, Bureau of Meteorology
X. Li, CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Center for Ocean Mega-Science, Chinese Academy of Sciences

The Indo-Pacific region is a nexus of atmospheric and oceanic processes generated and maintained on multiple spatial and temporal scales, and it also serves as a conduit for interocean exchanges of heat and salt, spreading a significant global impact. This session aims to foster interdisciplinary discussions and advance our ability to understand and predict the variations in the Indo-Pacific and other areas by connecting traditional sciences with the innovative power of machine learning. We invite contributions on (but not limited to): 1) Understanding of atmosphere-ocean processes using observations, numerical modeling, and data-driven approaches; 2) Predictions at different timescales from weather forecast, subseasonal to seasonal climate, and future projections; 3) Data qualification, reconstruction and assimilation for multiple sources of data provided by different monitoring platforms; 4) Monitoring, detection, classification, and segmentation of environmental disasters; 5) Dynamical model bias traceback, correcting and downscaling; 6) Explainable AI and 'physics + AI' hybrid modelling.

Index Terms
3314 Convective processes
3373 Tropical dynamics
4504 Air|sea interactions

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
National Climate Assessment
Machine Learning and AI

Cross-Listed:
NH - Natural Hazards
OS - Ocean Sciences

Neighborhoods:
3. Earth Covering