Seasonal forecasting: Difference between revisions
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==Background Readings== |
==Background Readings== |
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== Subseasonal Forecasting == |
=== Subseasonal Forecasting === |
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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" />. |
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" />. |
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