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ML is a suitable tool for making short-term weather predictions<ref>{{Cite web|url=https://www.ecmwf.int/en/elibrary/19877-machine-learning-ecmwf-roadmap-next-10-years|title=Machine learning at ECMWF: A roadmap for the next 10 years|last=Düben|first=Peter|last2=Modigliani|first2=Umberto|date=2021|website=www.ecmwf.int|access-date=2021-01-25|last3=Geer|first3=Alan|last4=Siemen|first4=Stephan|last5=Pappenberger|first5=Florian|last6=Bauer|first6=Peter|last7=Brown|first7=Andy|last8=Palkovic|first8=Martin|last9=Raoult|first9=Baudouin}}</ref> based on the observed initial conditions, and post-processing the output from weather models<ref>{{Cite journal|last=Rasp|first=Stephan|last2=Lerch|first2=Sebastian|date=2018-11-01|title=Neural Networks for Postprocessing Ensemble Weather Forecasts|url=https://journals.ametsoc.org/view/journals/mwre/146/11/mwr-d-18-0187.1.xml|journal=Monthly Weather Review|language=EN|volume=146|issue=11|pages=3885–3900|doi=10.1175/MWR-D-18-0187.1|issn=1520-0493}}</ref>.
 
ML is a suitable tool for making short-term weather predictions<ref>{{Cite web|url=https://www.ecmwf.int/en/elibrary/19877-machine-learning-ecmwf-roadmap-next-10-years|title=Machine learning at ECMWF: A roadmap for the next 10 years|last=Düben|first=Peter|last2=Modigliani|first2=Umberto|date=2021|website=www.ecmwf.int|access-date=2021-01-25|last3=Geer|first3=Alan|last4=Siemen|first4=Stephan|last5=Pappenberger|first5=Florian|last6=Bauer|first6=Peter|last7=Brown|first7=Andy|last8=Palkovic|first8=Martin|last9=Raoult|first9=Baudouin}}</ref> based on the observed initial conditions, and post-processing the output from weather models<ref>{{Cite journal|last=Rasp|first=Stephan|last2=Lerch|first2=Sebastian|date=2018-11-01|title=Neural Networks for Postprocessing Ensemble Weather Forecasts|url=https://journals.ametsoc.org/view/journals/mwre/146/11/mwr-d-18-0187.1.xml|journal=Monthly Weather Review|language=EN|volume=146|issue=11|pages=3885–3900|doi=10.1175/MWR-D-18-0187.1|issn=1520-0493}}</ref>.
==ML Application Areas==
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==ML Application areas==
 
*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
 
*'''[[Storm Tracking|Storm tracking]]''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes. Identifying extreme events in climate model outputs can inform scientific understanding of where and when these events may occur. ML can help classify, detect, and track climate-related extreme events such as hurricanes in climate model outputs.
 
*[[Dust storm Prediction]]: Dust storms affect people, their properties, and their activities. For this reason, it is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.
 
*[[Dust storm Prediction]]: Dust storms affect people, their properties, and their activities. For this reason, it is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.
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