[S2S Webinar Series] AIML methods for S2S prediction Video_Jan. 2021

PROGRAM

1) Peter Dueben (ECMWF)
Title: An overview on the use of machine learning to predict weather and climate
2) Marlene Kretshmer (U. Reading)
Title: Quantifying Teleconnection Pathways using Causal Networks
3) Zheng Wu (ETH Zurich)
Title: Extended-range predictability of stratospheric extreme events suggested by dynamical mode decomposition.
4) Michael Sheuerer
Title: Using artificial neural networks for generating probabilistic subseasonal precipitation forecasts over California

[Rainstorm]

Thank you very much. I have developed simple statistical model to predict weekly cyclone activity at country level as part of my PhD project. It isn't given the best results but there are some skills. The paper is submitted for peer review now. I wonder if any experts in AI or machine learning can voluntarily willing to collaborate with me to develop sub-seasonal prediction framework using machine learning or the appropriate method. It would be interesting to see the results for these two approaches at island level. Thanks.