A056
Machine Learning for Weather and Climate Modeling I

Tuesday, 8 December 2020: 20:30-21:30
Virtual
Primary Convener:  Noah Brenowitz, Vulcan, Climate Modeling, Seattle, WA, United States
Conveners:  Christopher Stephen Bretherton, University of Washington Seattle Campus, Seattle, WA, United States, Laure Zanna, University of Oxford, Dept. of Physics, Oxford, United Kingdom and Maike Sonnewald, Princeton University, Princeton, NJ, United States
Primary Liaison:  Noah Brenowitz, Vulcan, Climate Modeling, Seattle, WA, United States
Chairs:  Noah Brenowitz, Vulcan, Inc., Climate Modeling, Seattle, WA, United States and Laure Zanna, University of Oxford, Dept of Physics, Oxford, United Kingdom
OSPA Liaison:  Christopher Stephen Bretherton, University of Washington Seattle Campus, Seattle, WA, United States
20:30
A056-01
Hybrid Weather Prediction: A Blend of Machine Learning and Numerical Modeling (770450)
Troy Arcomano1, Istvan Szunyogh2, Edward Ott3, Brian Hunt4 and Alexander Wikner3, (1)Texas A&M University, College Station, TX, United States, (2)Texas A&M University, Atmospheric Science, College Station, TX, United States, (3)University of Maryland College Park, College Park, United States, (4)University of Maryland College Park, College Park, MD, United States
20:34
A056-02
Incorporating physical knowledge in machine learning parameterizations of convection (Invited) (666057)
Pierre Gentine, Columbia University, Earth and Environmental Engineering, New York, NY, United States, Tom Beucler, Massachusetts Institute of Technology, Cambridge, MA, United States, Mike S Pritchard, University California Irvine, Department of Earth System Science, Irvine, CA, United States and Veronika Eyring, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
20:38
A056-03
Machine Learning Physics Parametrisation:  Impact of pre-processing and architecture (724497)
Omar Jamil and Cyril Julien Morcrette, Met Office, Exeter, United Kingdom
20:42
A056-04
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions (713886)
Janni Yuval, Massachusetts Institute of Technology, Cambridge, United States and Paul A O'Gorman, Massachusetts Institute of Technology, Cambridge, MA, United States
20:46
A056-05
Performance of a Random Forest Parameterization in Predicting the Diurnal Cycle of Precipitation (701405)
Anna Kwa1, Noah Brenowitz2, Christopher Stephen Bretherton3, Spencer Clark2, Brian M Henn1, Jeremy McGibbon2, Andre Perkins2 and Oliver Watt-Meyer2, (1)Vulcan, Climate Modeling, Seattle, WA, United States, (2)Vulcan, Inc., Climate Modeling, Seattle, WA, United States, (3)University of Washington Seattle Campus, Seattle, WA, United States
20:50
A056-07
A Data-Driven, Single Column Gravity Wave Parameterization in an Idealized Model (728027)
Zac Espinosa, Stanford Earth Sciences, Stanford, CA, United States, Aditi Sheshadri, Stanford University, Department of Earth System Science, Stanford, CA, United States, Edwin P Gerber, New York University, Courant Institute of Mathematical Sciences, New York, NY, United States and Kevin DallaSanta, NASA Goddard Institute for Space Studies, New York, NY, United States
20:54
A056-08
Deep Learning for a stochastic subgrid parameterization of ocean momentum forcing (735659)
Arthur Guillaumin, New York University, New York, NY, United States and Laure Zanna, University of Oxford, Dept of Physics, Oxford, United Kingdom
20:58
Discussion
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