Seasonal forecasting: Difference between revisions

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(extended the application of ML to seasonal forecasting by introducing the challenge of polar vortex prediction. It may fall more into the extreme weather event prediction.)
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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>.
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Seasonal forecasting has traditionally been modeled using complex dynamical models, rather than statistical methods, often called [https://en.wikipedia.org/wiki/General_circulation_model general circulation models] (GCMs). However, seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) and polar vortices, are difficult to predict using traditional methods. ML and deep learning can improve our forecasting of multi-year ENSO events <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> and polar vortices<ref>{{Cite journal|last=Cohen|first=Judah|last2=Coumou|first2=Dim|last3=Hwang|first3=Jessica|last4=Mackey|first4=Lester|last5=Orenstein|first5=Paulo|last6=Totz|first6=Sonja|last7=Tziperman|first7=Eli|date=2019|title=S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/wcc.567|journal=WIREs Climate Change|language=en|volume=10|issue=2|pages=e00567|doi=10.1002/wcc.567|issn=1757-7799}}</ref>.
   
 
==Background Readings==
 
==Background Readings==

Revision as of 19:21, 26 March 2021

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Seasonal forecasting has traditionally been modeled using complex dynamical models, rather than statistical methods, often called general circulation models (GCMs). However, seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) and polar vortices, are difficult to predict using traditional methods. ML and deep learning can improve our forecasting of multi-year ENSO events [1][2][3][4][5] and polar vortices[6].

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.
  6. Cohen, Judah; Coumou, Dim; Hwang, Jessica; Mackey, Lester; Orenstein, Paulo; Totz, Sonja; Tziperman, Eli (2019). "S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts". WIREs Climate Change. 10 (2): e00567. doi:10.1002/wcc.567. ISSN 1757-7799.