Using machine learning to make skillful predictions of the wintertime North Atlantic Oscillation

Add to your list(s) Download to your calendar using vCal

• James Keeble, Chemistry dept
• Thursday 22 August 2019, 13:00-14:00
• Newnham Terrace Seminar Room, Darwin .

If you have a question about this talk, please contact Rachel Furner.

Using machine learning to make skillful predictions of the wintertime North Atlantic Oscillation, James Keeble, NCAS , J. Keeble, Y. Y. S. Yiu, P. J. Nowack, P. T. Griffiths, and J. A. Pyle. The North Atlantic Oscillation (NAO) has a well-documented effect on wintertime climate in both Europe and North America. It has also been suggested, using statistical techniques and climate models, to possess a degree of predictability on seasonal timescales, an important consideration when planning for potential financial, ecological and human health impacts of wintertime weather. Here, we explore the use of Machine Learning (ML) techniques, specifically Ridge regression, to enhance and understand seasonal predictability of the wintertime NAO index. We find that ML achieves similar predictive skill to other methods (r≈0.6). Furthermore, we show that ML successfully identifies those regions which have been shown in other studies to be important for deriving predictability of the NAO index. We conclude that Ridge regression is a promising alternative technique for making seasonal forecasts of the wintertime NAO and for identifying the sources of this predictability.

This talk is part of the AI4ER Seminar Series series.

This talk is included in these lists:

• AI4ER Seminar Series
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Newnham Terrace Seminar Room, Darwin
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.