Session Explainable Artificial Intelligence (XAI) for Environmental Science


Description:

This session invites abstracts on projects utilizing explainable artificial intelligence methods in their models and/or post processing applications. We particularly encourage abstracts that describe how the XAI methods impacted the final model development and analysis and/or were utilized with model users in mind.


Papers:
Distinguishing the Regional Emergence of United States Summer Temperatures Between Observations and Climate Model Large Ensembles
Zachary Michael Labe, PhD, Princeton University, Princeton, NJ; and N. Johnson and T. L. Delworth

Explainable deep learning for climate applications using the Spectral Analysis of Regression activations and Kernels (SpARK) framework
Yifei Guan, Rice University, Houston, TX; and A. K. Chattopadhyay, A. Subel, H. Alizadeh Pahlavan, and P. Hassanzadeh

Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification
Ryan A. Lagerquist, CIRA and NOAA/ESRL/GSL, Boulder, CO; and J. Knaff, C. Slocum, K. Musgrave, and I. Ebert-Uphoff

Submission Deadline: Thursday, 31 August 2023



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