Joint Session 60 Machine Learning for Subgrid Parameterization in Weather and Climate Models

Thursday, 16 January 2020: 10:30 AM-12:00 PM
Hosts: (Joint between 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) )
Ryan A. Lagerquist, CIMMS, Meteorology, Norman, OK; Christiane Jablonowski, University of Michigan, Climate and Space Science and Enigneering, Ann Arbor, MI and Carlos F. Gaitan, Arable Labs, Inc., Machine Learning and Artificial Intelligence, Princeton, NJ

This session will feature presentations on using machine learning models for parameterizing subgrid processes in numerical weather and climate models.

10:30 AM
Building a Hierarchy of Hybrid, Neural-network Parametrizations of Convection
Tom Beucler, UCI, Irvine, CA; Columbia University, New York, CA; and P. Gentine, M. S. Pritchard, S. Rasp, and V. Eyring

10:45 AM
Data-driven super-parameterization using deep learning: Experimentations with a multi-scale Lorenz 96 model
Pedram Hassanzadeh, Rice University, 6100 Main St., Houston, TX; and A. Chattopadhyay, A. Subel, and K. Palem

11:00 AM
Machine Learning Parameterization of the Surface Layer: Integration with WRF
David John Gagne II, NCAR, Boulder, CO; and T. C. McCandless, B. Kosovic, A. DeCastro, R. D. Loft, S. E. Haupt, and B. Yang

11:15 AM
Data-driven approaches for simulating rainfall in climate models
R. Saravanan, Texas A&M Univ., College Station, TX; and J. Yang, M. Jun, C. Schumacher, J. Wang, and R. K. W. Wang

11:30 AM
Towards sub-grid scale parameterizations using a super-resolution generative adversarial network
Eden Au, University of Edinburgh, Edinburgh, United Kingdom; and K. Kashinath, A. Albert, M. Prabhat, and S. F. B. Tett

11:45 AM
Utilizing Machine Learning to Replace Physical Parameterization Schemes: How do Different Techniques Compare?
Garrett Limon, University of Michigan, Ann Arbor, MI; and C. Jablonowski

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