Difference between revisions of "Seasonal forecasting"

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{{Disclaimer}}
 
{{Disclaimer}}
   
Seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting<ref>{{Cite journal|last=Ham|first=Yoo-Geun|last2=Kim|first2=Jeong-Hwan|last3=Luo|first3=Jing-Jia|date=2019-09|title=Deep learning for multi-year ENSO forecasts|url=https://www.nature.com/articles/s41586-019-1559-7|journal=Nature|language=en|volume=573|issue=7775|pages=568–572|doi=10.1038/s41586-019-1559-7|issn=1476-4687}}</ref><ref>{{Cite journal|last=Toms|first=Benjamin A.|last2=Barnes|first2=Elizabeth A.|last3=Ebert‐Uphoff|first3=Imme|date=2020|title=Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS002002|journal=Journal of Advances in Modeling Earth Systems|language=en|volume=12|issue=9|pages=e2019MS002002|doi=10.1029/2019MS002002|issn=1942-2466}}</ref><ref>{{Cite web|url=https://www.climatechange.ai/papers/neurips2019/40/paper.pdf|title=Forecasting El Niño with Convolutional andRecurrent Neural Networks|last=Mahesh,|first=A., et al.,|date=2019|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite web|url=https://arxiv.org/pdf/2012.01598.pdf|title=Graph Neural Networks for Improved El NiñoForecasting|last=Cachay,|first=S. R. et al|date=2020|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Guo|first=Yanan|last2=Cao|first2=Xiaoqun|last3=Liu|first3=Bainian|last4=Peng|first4=Kecheng|date=2020/6|title=El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition|url=https://www.mdpi.com/2073-8994/12/6/893|journal=Symmetry|language=en|volume=12|issue=6|pages=893|doi=10.3390/sym12060893}}</ref>.
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Seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting<ref>{{Cite journal|last=Ham|first=Yoo-Geun|last2=Kim|first2=Jeong-Hwan|last3=Luo|first3=Jing-Jia|date=2019|title=Deep learning for multi-year ENSO forecasts|url=https://www.nature.com/articles/s41586-019-1559-7|journal=Nature|language=en|volume=573|issue=7775|pages=568–572|doi=10.1038/s41586-019-1559-7|issn=1476-4687|via=}}</ref><ref>{{Cite journal|last=Toms|first=Benjamin A.|last2=Barnes|first2=Elizabeth A.|last3=Ebert‐Uphoff|first3=Imme|date=2020|title=Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS002002|journal=Journal of Advances in Modeling Earth Systems|language=en|volume=12|issue=9|pages=e2019MS002002|doi=10.1029/2019MS002002|issn=1942-2466}}</ref><ref>{{Cite web|url=https://www.climatechange.ai/papers/neurips2019/40/paper.pdf|title=Forecasting El Niño with Convolutional andRecurrent Neural Networks|last=Mahesh,|first=A., et al.,|date=2019|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite web|url=https://arxiv.org/pdf/2012.01598.pdf|title=Graph Neural Networks for Improved El NiñoForecasting|last=Cachay,|first=S. R. et al.,|date=2020|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref><ref>{{Cite journal|last=Guo|first=Yanan|last2=Cao|first2=Xiaoqun|last3=Liu|first3=Bainian|last4=Peng|first4=Kecheng|date=2020|title=El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition|url=https://www.mdpi.com/2073-8994/12/6/893|journal=Symmetry|language=en|volume=12|issue=6|pages=893|doi=10.3390/sym12060893|via=}}</ref>.
   
 
==Background Readings==
 
==Background Readings==

Revision as of 18:41, 25 January 2021

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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.

Seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting[1][2][3][4][5].

Background Readings

Conferences, Journals, and Professional Organizations

Libraries and Tools

Data

Future Directions

Relevant Groups and Organizations

References

  1. Ham, Yoo-Geun; Kim, Jeong-Hwan; Luo, Jing-Jia (2019). "Deep learning for multi-year ENSO forecasts". Nature. 573 (7775): 568–572. doi:10.1038/s41586-019-1559-7. ISSN 1476-4687.
  2. Toms, Benjamin A.; Barnes, Elizabeth A.; Ebert‐Uphoff, Imme (2020). "Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability". Journal of Advances in Modeling Earth Systems. 12 (9): e2019MS002002. doi:10.1029/2019MS002002. ISSN 1942-2466.
  3. Mahesh,, A., et al., (2019). "Forecasting El Niño with Convolutional andRecurrent Neural Networks" (PDF).CS1 maint: extra punctuation (link)
  4. Cachay,, S. R. et al., (2020). "Graph Neural Networks for Improved El NiñoForecasting" (PDF).CS1 maint: extra punctuation (link)
  5. Guo, Yanan; Cao, Xiaoqun; Liu, Bainian; Peng, Kecheng (2020). "El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition". Symmetry. 12 (6): 893. doi:10.3390/sym12060893.