Joint Session 37 Physical Interpretability in Machine Learning

Wednesday, 15 January 2020: 8:30 AM-10:00 AM
260 (Boston Convention and Exhibition Center)
Hosts: (Joint between the 26th Conference on Probability and Statistics; the 19th Conference on Artificial Intelligence for Environmental Science; and the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP) )
Cochairs:
Elizabeth Satterfield, NRL, Monterey, CA and Philippe Tissot, Texas A&M Univ. - Corpus Christi, Conrad Blucher Institute, Corpus Christi, TX

Physical interpretability in machine learning

Papers:
8:30 AM
J37.1
Multiresolution Cluster Analysis—Addressing Trust in Climate Classification
Derek DeSantis, LANL, Los Alamos, NM; and P. Wolfram and B. Alexandrov
Recorded Presentation

8:45 AM
J37.2
Understanding What Deep Learning Has Learned about Tornadoes
Ryan A. Lagerquist, CIMMS, Norman, OK; and A. McGovern, D. J. Gagne II, C. R. Homeyer, and T. M. Smith
Recorded Presentation

9:00 AM
J37.3
Selected Methods from Explainable AI to Improve Understanding of Neural Network Reasoning for Environmental Science Applications
Imme Ebert-Uphoff, CIRA–Colorado State Univ., Fort Collins, CO; and K. Hilburn, B. A. Toms, and E. A. Barnes
Recorded Presentation

9:15 AM
J37.4
Emulation of Bin Microphysical Processes with Machine Learning
David John Gagne II, NCAR, Boulder, CO; and C. C. Chen and A. Gettelman
Recorded Presentation

9:30 AM
J37.5
Using Physically Interpretable Neural Networks to Discover Modes of Climate and Weather Variability
Benjamin A. Toms, Colorado State Univ., Fort Collins, CO; and E. A. Barnes and I. Ebert-Uphoff
Recorded Presentation

9:45 AM
J37.6
Lessons Learned Using ML for Knowledge Discovery in the Atmospheric Sciences
Amy McGovern, Univ. of Oklahoma, Norman, OK
Recorded Presentation

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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