Weather forecasting: Difference between revisions

 

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==

*'''[[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.

==Background Readings==