Joint Session 66 Machine Learning for Subgrid Parameterization in Weather and Climate Models

Thursday, 16 January 2020: 10:30 AM-12:00 PM
156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP); and the Events )
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 Parameterizations of Convection
Tom Beucler, Univ. of California, Irvine, Irvine, CA; Columbia Univ., New York, CA; and P. Gentine, M. S. Pritchard, S. Rasp, and V. Eyring
10:45 AM
Data-Driven Superparameterization Using Deep Learning: Experimentations with a Multiscale Lorenz 96 Model
Pedram Hassanzadeh, Rice University, 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
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner