Session 9B Machine Learning for Subseasonal to Seasonal Prediction

Wednesday, 15 January 2020: 1:30 PM-2:30 PM
156BC (Boston Convention and Exhibition Center)
Hosts: (Joint between the 19th Conference on Artificial Intelligence for Environmental Science; and the Events )
Cochairs:
Carlos F. Gaitan, Arable Labs, Inc., Machine Learning and Artificial Intelligence, Princeton, NJ and Maria J. Molina, AccuWeather, Inc., Office of Organizational Excellence, State College, PA

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

Papers:
1:30 PM
9B.1
Applying Machine Learning to Improve Subseasonal-to-Seasonal (S2S) Forecasts
Soukayna Mouatadid, Univ. of Toronto, Toronto, ON, Canada; and J. Cohen and L. Mackey
Recorded Presentation

1:45 PM
9B.2
Using Machine Learning to Improve Subseasonal-to-Seasonal (S2S) Prediction
Richard Garmong, Univ. of Georgia, Athens, GA; and R. Bolinger and R. S. Schumacher
Recorded Presentation

9B.3
Basin of Prediction for Seasonal Weather Forecasting Using Self-Similar Power Transforms

2:00 PM
9B.4
Applications of Deep Learning to S2S Precipitation Prediction and Downscaling for the Middle East and North Africa
Hamada S. Badr, The 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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