Call for abstract - AGU2024 | H051 Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models

[Juliane Mai]

Hi everyone,

we are excited to have again a session on "Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models" at AGU this year. We are looking forward to your submissions and hearing about your amazing research.

Session & Submission: https://agu.confex.com/agu/agu24/prelim.cgi/Session/227425

Description:

As science and engineering are used to understand, plan for, and resolve societal problems, process-based, AI/ML, and hybrid models are key simulation and prediction tools.

For all models, inputs and structure tend to be complex and have errors, which propagate into uncertain model outputs. In addition, models continually increase in computational demand with the pursuit of greater realism and integration of more and different data. Sensitivity and Uncertainty Analyses (SA/UA) are used to understand the implications of choices around model structure, identify important model parameters and data correlations, and characterize uncertainties.

This session invites contributions on both theory and application of model diagnostics, sensitivity and uncertainty analysis. We further welcome contributions that explore the model-data interface, which plays an important role in uncertainty quantification.

Particular topics of interest include:

• AI/ML techniques for exploring extensive datasets
  
• Strategies for more comprehensive SA/UA
  
• Input data error quantification
  
• Improving computational efficiency of SA/UA
  

Conveners:
Juliane Mai (University of Waterloo, Canada)
Lijing Wang (University of Connecticut, Unites States)
Lucy Marshall (Macquarie University, Australia)
Mary C Hill (University of Kansas, United States)