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
Cochairs:
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.

Papers:
1:30 PM
9B.1
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
9B.2
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
9B.3
Basin of Prediction for Seasonal Weather Forecasting using Self-Similar Power Transforms
M. Jeremie Lafitte (Levitas), Metivdata, Safed, Israel

2:15 PM
9B.4
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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