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SUMMARY:Deep learning for multi-year ENSO forecasts
DTSTART;VALUE=DATE-TIME:20191107T021000Z
DTEND;VALUE=DATE-TIME:20191107T023000Z
DTSTAMP;VALUE=DATE-TIME:20260723T014000Z
UID:indico-contribution-21@sshep.snu.ac.kr
DESCRIPTION:Speakers: Yoo-Geun Ham (Chonnam National University)\nVariatio
 ns in the El Niño/Southern Oscillation (ENSO) are associated with a wide 
 array of regional climate extremes and ecosystem impacts. Robust\, long-le
 ad forecasts would therefore be valuable for managing policy responses. Bu
 t despite decades of effort\, forecasting ENSO events at lead times of mor
 e than one year remains problematic. Here we show that a statistical forec
 ast model employing a deep-learning approach produces skilful ENSO forecas
 ts for lead times of up to one and a half years. To circumvent the limited
  amount of observation data\, we use transfer learning to train a convolut
 ional neural network (CNN) first on historical simulations and subsequentl
 y on reanalysis from 1871 to 1973. During the validation period from 1984 
 to 2017\, the all-season correlation skill of the Nino3.4 index of the CNN
  model is much higher than those of current state-of-the-art dynamical for
 ecast systems. The CNN model is also better at predicting the detailed zon
 al distribution of sea surface temperatures\, overcoming a weakness of dyn
 amical forecast models. A heat map analysis indicates that the CNN model p
 redicts ENSO events using physically reasonable precursors. The CNN model 
 is thus a powerful tool for both the prediction of ENSO events and for the
  analysis of their associated complex mechanisms.\n\n\nReferences:  \n\n[h
 ttps://www.nature.com/articles/s41586-019-1559-7][1]\n\n[https://www.scien
 cemag.org/news/2019/09/artificial-intelligence-could-predict-el-ni-o-18-mo
 nths-advance][2]\n\n[https://www.yna.co.kr/view/AKR20190918093900017?input
 =1195m][3]\n\n[http://biz.chosun.com/site/data/html_dir/2019/09/19/2019091
 900659.html][4]\n\n\n  [1]: https://www.nature.com/articles/s41586-019-155
 9-7\n  [2]: https://www.sciencemag.org/news/2019/09/artificial-intelligenc
 e-could-predict-el-ni-o-18-months-advance\n  [3]: https://www.yna.co.kr/vi
 ew/AKR20190918093900017?input=1195m\n  [4]: http://biz.chosun.com/site/dat
 a/html_dir/2019/09/19/2019091900659.html\n\nhttps://sshep.snu.ac.kr/event/
 107/contributions/21/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/21/
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