Primary Convener:
Mukesh Kumar, University of California Merced

Convener:
Moji Sadegh, Boise State University
Sridhara Nayak, Japan Meteorological Corporation Limited
Raju Attada, Indian Institute of Science Education and Research Mohali

Early Career Convener:
Somnath Mondal, Northeastern University

Chair:
Moji Sadegh, Boise State University
Mukesh Kumar, University of California Merced

The growing severity of weather and climate extremes brings into focus the importance of using data and diverse expertise to explore their connected risks. This session aims to explore how data-driven methods can reveal hidden patterns behind destructive meteorological events—such as prolonged dry and wet spells, high-impact winds, fire outbreaks, and intense heat episodes—that often occur together or in sequence. We welcome studies that use observational records, empirical analysis, and AI-based tools to investigate real-world event chains and their effects on people, ecosystems, and infrastructure. Rather than focusing on simulation or forecasting, we emphasize grounded, data-centric insights into event triggers, compounding effects, and local-to-global consequences. Contributions that link physical processes with social or environmental outcomes are especially encouraged.

Index Terms
4313 Extreme events
4315 Monitoring, forecasting, prediction
4321 Climate impact
4328 Risk

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Climate Change and Global Policy
Machine Learning and AI
Open Science and Open Data

Neighborhoods:
1. Science Nexus