Session 2A Applications of Machine Learning in Earth System Modeling

Monday, 13 January 2020: 2:00 PM-4:00 PM
156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs:
Christiane Jablonowski, University of Michigan, Climate and Space Science and Enigneering, Ann Arbor, MI and Christoph A. Keller, USRA/GESTAR NASA/GMAO, Greenbelt, MD

This session features applications of machine learning and deep learning to atmospheric, ocean, land, and ice models.

Papers:
2:00 PM
2A.1
Discovering Novel Eddy Parameterizations with Machine Learning
Laure Zanna, Univ. of Oxford, Oxford, United Kingdom; and T. Bolton
Recorded Presentation

2:15 PM
2A.2
A Pure Deep Learning Approach to Precipitation Nowcasting
Jason Hickey, Google, Mountain View, CA; and C. Gazen, S. Agrawal, C. Bromberg, L. Barrington, V. Lakshmanan, and J. Burge
Recorded Presentation

2:30 PM
2A.3
Toward Physics-Informed Deep Learning for Turbulent Flows
Rui Wang, Northeastern Univ., Boston, MA; and A. Albert, K. Kashinath, M. Mustafa, and R. Yu
Recorded Presentation Manuscript (35.6 kB)

2:45 PM
2A.4
Deep Learning for Weather Prediction: Forecasting Globally Gridded 500-hPa Geopotential Heights on Short- to Medium-Range Time Scales
Jonathan A. Weyn, Univ. of Washington, Seattle, WA; and D. R. Durran and R. Caruana
Recorded Presentation

3:00 PM
2A.5
Nonlinear Averaging of Global NCEP Wave Ensemble Using NNs
Vladimir Krasnopolsky, NOAA, College Park, MD
Recorded Presentation

3:15 PM
2A.6
Machine Learning for Parameterization of Moist Processes in the Atmosphere
Janni Yuval, MIT, Cambridge, MA; and P. A. O'Gorman
Recorded Presentation

3:30 PM
2A.7
Developing the Snow Cover Fraction Schemes for Land Surface Models Using a Machine Learning Approach
Yuan-Heng Wang, The Univ. of Arizona, Tucson, AZ; and H. V. Gupta, P. D. Broxton, Y. Fang, A. Behrangi, X. Zeng, and G. Y. Niu
Recorded Presentation

2A.8
A Machine Learning–Based Parameterization of OH

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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