Editing Seasonal forecasting

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=== Subseasonal Forecasting ===
 
=== 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 name="hwang2019improving">{{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" />.
 
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 name="hwang2019improving">{{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" />.
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*'''"Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances" (2020) '''<ref name=":0" />: A review showing that machine learning 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.
 
*'''"Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances" (2020) '''<ref name=":0" />: A review showing that machine learning 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.
 
* '''"Improving Subseasonal Forecasting in the Western U.S. with Machine Learning" (2019) '''<ref name="hwang2019improving />: The paper develops and applies two distinct nonlinear regression models to the western United States, both of which outperform the baseline model significantly.
 
* '''"Improving Subseasonal Forecasting in the Western U.S. with Machine Learning" (2019) '''<ref name="hwang2019improving />: The paper develops and applies two distinct nonlinear regression models to the western United States, both of which outperform the baseline model significantly.
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