Seasonal forecasting
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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].
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Conferences, Journals, and Professional Organizations
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References
- ↑ 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.
- ↑ 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.
- ↑ Mahesh,, A., et al., (2019). "Forecasting El Niño with Convolutional andRecurrent Neural Networks" (PDF).CS1 maint: extra punctuation (link)
- ↑ Cachay,, S. R. et al., (2020). "Graph Neural Networks for Improved El NiñoForecasting" (PDF).CS1 maint: extra punctuation (link)
- ↑ 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.
- ↑ 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.