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SUMMARY:Training course: Machine learning for weather prediction
DTSTART:20251027T083000Z
DTEND:20251031T180000Z
DTSTAMP:20260518T183900Z
UID:indico-event-466@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:\nThis five-day course focuses on machine learning for numeric
 al weather prediction (NWP). This will include:\n\nAn overview on the use 
 of machine learning in Earth Sciences\nIntroduction into the most importan
 t machine learning methods that are relevant for Earth Sciences.\nIntroduc
 tion into software and hardware frameworks at ECMWF to facilitate the use 
 of machine learning.\nExamples for the use of specific machine learning to
 ols across the weather and climate prediction workflow and how they can be
  prepared for use in operational predictions.\n\nAs there are many general
  courses on machine learning available – including free online courses 
 – this course will have a particular focus on the use of machine learnin
 g in the domain of Earth Sciences.\nAs well as lectures there will be disc
 ussions and hands-on sessions.\nMain topics\n\nAn overview on the use of m
 achine learning in Earth Sciences\nAvailable datasets and how to retrieve 
 them\nSoftware and hardware for machine learning in NWP\nMachine learning 
 models (in the context of Earth sciences)\, including deep learning approa
 ches\nData-driven forecasting\, based around AIFS and the Anemoi framework
 \nFurther examples of machine learning applications\n\nRequirements\nParti
 cipants should have a good meteorological or a good machine learning backg
 round\, or both. Participants should also have some limited experience wit
 h Python code and Jupyter notebooks. Basic experience with machine learnin
 g applications in Earth system sciences and the handling of Earth system d
 ata would be advantageous. Some practical experience in numerical weather 
 prediction is an advantage.\nAll lectures are in English.\nLinks to code e
 xamples for practical sessions\nSee the course repo.\n\nhttps://events.ecm
 wf.int/event/466/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/466/logo-3547453490.png
LOCATION:ECMWF 
URL:https://events.ecmwf.int/event/466/
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