Seasonal forecasting: Difference between revisions
added subsection on Subseasonal forecasting w/ 2 papers and a dataset
(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.) |
(added subsection on Subseasonal forecasting w/ 2 papers and a dataset) |
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==Background Readings==
== Subseasonal Forecasting ==
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 <ref name=":0">{{Cite journal|last=He|first=Sijie|last2=Li|first2=Xinyan|last3=DelSole|first3=Timothy|last4=Ravikumar|first4=Pradeep|last5=Banerjee|first5=Arindam|date=2020-06-24|title=Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances|url=http://arxiv.org/abs/2006.07972|journal=arXiv:2006.07972 [cs, stat]}}</ref>. Improvements in subseasonal prediction will be realized in industries such as water management <ref>{{Cite journal|last=Hwang|first=Jessica|last2=Orenstein|first2=Paulo|last3=Cohen|first3=Judah|last4=Pfeiffer|first4=Karl|last5=Mackey|first5=Lester|date=2019-07-25|title=Improving Subseasonal Forecasting in the Western U.S. with Machine Learning|url=https://doi.org/10.1145/3292500.3330674|journal=Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining|series=KDD '19|location=Anchorage, AK, USA|publisher=Association for Computing Machinery|pages=2325–2335|doi=10.1145/3292500.3330674|isbn=978-1-4503-6201-6}}</ref>, agricultural productivity, and emergency planning for extreme weather events <ref name=":0" />.
* In [7], 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 [8], 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==
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==Data==
* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG 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<ref>{{Cite journal|last=Hwang|first=Jessica|last2=Orenstein|first2=Paulo|last3=Cohen|first3=Judah|last4=Mackey|first4=Lester|date=2019-09-24|title=The SubseasonalRodeo Dataset|url=https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG|language=en|doi=10.7910/DVN/IHBANG}}</ref>.
==Future Directions==
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