Session 9B Machine Learning for Subseasonal to Seasonal Prediction

Wednesday, 15 January 2020: 1:30 PM-2:30 PM
Host: 19th Conference on Artificial Intelligence for Environmental Science
Carlos F. Gaitan, Arable Labs, Inc., Machine Learning and Artificial Intelligence, Princeton, NJ and Maria J. Molina, NCAR, Boulder, CO

Applications of Machine Learning techniques for pre-processing, post-processing and forecasting of S2S environmental variables.

1:30 PM
Applying Machine Learning to Improve Subseasonal to Seasonal (S2S) Forecasts
Soukayna Mouatadid, University of Toronto, Toronto, ON, Canada; and J. Cohen and L. Mackey

1:45 PM
Using Machine Learning to Improve Sub-Seasonal to Seasonal Prediction (S2S)
Richard Garmong, University of Georgia, Athens, GA; and R. Bolinger and R. S. Schumacher

2:00 PM
2:15 PM
Applications of Deep Learning to S2S Precipitation Prediction and Downscaling for the Middle East and North Africa
Hamada S. Badr, Johns Hopkins Univ., Baltimore, MD; and K. Bergaoui, B. F. Zaitchik, A. Hazra, A. McNally, C. D. Peters-Lidard, and R. McDonnell

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