Session: Machine Learning for Subgrid Parameterization in Weather and Climate Models (100th American Meteorological Society Annual Meeting)

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 )

Cochairs:

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.

Papers:

10:30 AM

J66.1

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

Recorded Presentation

10:45 AM

J66.2

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

J66.3