Enter Note Done
Go to previous page in this tab
Session
  • Oral
  • Bookmark Icon
  • SY51A: AI and High-Resolution Forecasting for Societal Resilience II Oral
  • Schedule
    Notes
  • Location Icon338-339
    NOLA CC
    Set Timezone
  •  
    View Map

Generic 'disconnected' Message
Primary Convener:
Sridhara Nayak, Japan Meteorological Corporation

Convener:
Mohan Kumar Das, National Oceanographic And Maritime Institute (NOAMI)
Netrananda Sahu, University of Delhi
Pawan Kumar Chaubey, Banaras Hindu University, DST-Mahamana Centre of Excellence in Climate Change Research

Early Career Convener:
Suman Maity, NIES National Institute of Environmental Studies

Chair:
Mohan Kumar Das, National Oceanographic And Maritime Institute (NOAMI)
Marcello Sano, University Ca' Foscari of Venice

In recent years, intensifying floods, droughts, mesoscale convective systems, storms, and heatwaves have highlighted the urgent need to turn scientific advances into practical tools that protect societies and save lives. This session aims to enhance how artificial intelligence (AI), machine learning (ML), and high-resolution modeling are being used to support monitoring control systems, early warning systems, disaster preparedness, and adaptive planning in societies facing all types of hydrometeorological extreme events and associated risks. We welcome contributions that highlight cross-sector collaboration, co-designed tools with decision-makers, and efforts to build trust in high-resolution models and AI-enhanced forecasts among the public and stakeholders. Case studies from vulnerable or data-scarce regions, and examples of integrating science into policy or emergency response, are especially encouraged. This session invites researchers, practitioners, and community leaders to share knowledge on bridging the gap between prediction and protection that truly supports societal resilience.

Index Terms
1637 Regional climate change
4313 Extreme events
4327 Resilience
4341 Early warning systems

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Climate Change and Global Policy
Science Communications
Machine Learning and AI
Global Impacts‚ Solutions‚ & Policies

Cross-Listed:
NH - Natural Hazards
H - Hydrology
GC - Global Environmental Change

Co-Sponsored Sessions:
JpGU: Japan Geoscience Union
EGU: European Geosciences Union
AOGS: Asia Oceania Geosciences Society
WCRP: World Climate Research Programme

Neighborhoods:
1. Science Nexus

Scientific Discipline
Suggested Itineraries
Neighborhood
Type
Where to Watch
Presentations
Discussion