Session Applying Uncertainty Quantification Methods to Environmental Artificial Intelligence Models


Description:

We invite abstracts that have experimented with AI uncertainty quantification methods applied to a variety of environmental science topics. In particular, we encourage abstracts highlighting what comes next after the uncertainty quantification and how to use the information gathered in model application.


Papers:
Estimating Uncertainty of Water Temperature Predictions for Cold-Stunning Events in the Laguna Madre
Hector Miguel Marrero-Colominas, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX; and M. Shotande, A. H. Fagg, M. White, P. Tissot, and A. McGovern

Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML
Ryan A. Lagerquist, CIRA and NOAA/ESRL/GSL, Boulder, CO; and D. D. Turner, J. Q. Stewart, and I. Ebert-Uphoff

Uncertainty Quantifications of the Onset and Offset of Cold-Stunning Events Using AI Ensemble Methods
Miranda White, Texas A&M University-Corpus Christi, Corpus Christi, TX; NSF Artificial Intelligence Institute (AI2ES), Corpus Christi, TX; and M. Vicens-Miquel, H. Marrero, P. E. Tissot, J. Woodall, C. Duff, and B. Colburn

Submission Deadline: Thursday, 31 August 2023