Primary Convener:
Soyoung Ha, NSF National Center for Atmospheric Research

Convener:
Moha Gharamti, NSF National Center for Atmospheric Research
Steven Fletcher, Cooperative Institute for Research in the Atmosphere

Chair:
Soyoung Ha, NCAR
Moha Gharamti, NSF National Center for Atmospheric Research

Data Assimilation (DA) is essential for Earth System Prediction (ESP), constraining models with observations for accurate state analysis by optimally blending forecasts and data. Significant challenges persist, particularly in using real-world data and applying DA to complex scenarios or extreme events. Key difficulties include characterizing model error, representing realistic background error covariances (B), observation operators (H), and observation errors (R). These are amplified in coupled DA due to cross-component interactions, complicating error modeling and limiting Earth System Prediction (ESP) skill. This session seeks diverse approaches, including ML/AI, to address these issues by learning error representations, emulating components, improving quality control, and tackling coupled DA problems. We invite submissions showcasing theoretical, methodological, and applied DA advancements across all Earth system domains and timescales that demonstrably improve DA performance and prediction skill. The goal is fostering discussion on progress, challenges (like physical consistency, uncertainty), and innovative solutions, including ML/AI integration, for ESP.

Index Terms
3315 Data assimilation
1816 Estimation and forecasting
4430 Complex systems
4499 General or miscellaneous

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

Cross-Listed:
P - Planetary Sciences
A - Atmospheric Sciences
H - Hydrology
GC - Global Environmental Change

Co-Organized Sessions:
Atmospheric Sciences

Neighborhoods:
3. Earth Covering
1. Science Nexus