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Atmospheric Physics-Guided Machine Learning: Towards Physically-Consistent, Data- Driven, and Interpretable Models of Convection
Tuesday, 11 May 2021, 1:00
Tuesday, May 11, 2021. 1:00 PM Eastern. Atmospheric Physics-Guided Machine Learning: Towards Physically-Consistent, Data-Driven, and Interpretable Models of Convection. Tom Beucler, University of California Irvine & Columbia University. Sponsored by NCAR/UCAR. More information here.
Data-driven algorithms, in particular neural networks, can emulate the effect of unresolved processes in coarse-resolution climate models if trained on high- resolution simulation or observational data. However, they lack interpretability, may violate key physical constraints, and make large errors when evaluated outside of their training set. In this seminar, I will share recent progress towards overcoming these three challenges in the particular case of machine learning the effect of subgrid-scale convection and clouds on the large-scale climate.