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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 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  and polar vortices.
Subseasonal forecasting is the task of predicting the climate of a region between 2-8 weeks in advance. Weather and seasonal prediction which focus on forecasting climate 1-7 days and 2+ months in advance respectively have already received significant attention and are considered easier prediction problems than the subseasonal scenario . Improvements in subseasonal prediction will be realized in industries such as water management , agricultural productivity, and emergency planning for extreme weather events .
- In , the authors show that ML models can be applied generally to the subseasonal forecasting problem context and they highlight the potential for tailored models to make large improvements over existing methods.
- In , Hwang et al. develop and apply two distinct nonlinear regression models to the western United States, both of which outperform the baseline model significantly.
Conferences, Journals, and Professional Organizations
Libraries and Tools
- SubseasonalRodeo: A benchmark dataset consisting of 12 features (including temperature, precipitation, humidity, etc.) from 14 different data sources used for training and evaluating subseasonal forecast systems in the contiguous western United States.
Relevant Groups and Organizations
- 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.
- He, Sijie; Li, Xinyan; DelSole, Timothy; Ravikumar, Pradeep; Banerjee, Arindam (2020-06-24). "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances". arXiv:2006.07972 [cs, stat].
- Hwang, Jessica; Orenstein, Paulo; Cohen, Judah; Pfeiffer, Karl; Mackey, Lester (2019-07-25). "Improving Subseasonal Forecasting in the Western U.S. with Machine Learning". Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD '19. Anchorage, AK, USA: Association for Computing Machinery: 2325–2335. doi:10.1145/3292500.3330674. ISBN 978-1-4503-6201-6.
- Hwang, Jessica; Orenstein, Paulo; Cohen, Judah; Mackey, Lester (2019-09-24). "The SubseasonalRodeo Dataset". doi:10.7910/DVN/IHBANG. Cite journal requires