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SUMMARY:Training course: Machine learning and Destination Earth
DTSTART:20260223T113000Z
DTEND:20260227T200000Z
DTSTAMP:20260807T220500Z
UID:indico-event-495@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:\nThis five-day course focuses on machine learning (ML) for nu
 merical weather prediction (NWP) and earth system modelling\, in the conte
 xt of the Destination Earth programme.\nDestination Earth (DestinE) is an 
 EU-funded initiative to develop digital twins of the Earth to better model
  and understand climate change and extreme weather. It is jointly implemen
 ted by ECMWF\, EUMETSAT and the European Space Agency.\nAI and ML are incr
 easingly playing a central role in DestinE\, and are being used to build M
 L-based earth system components such as ocean\, sea ice and waves to compl
 ement the physics-based models underpinning DestinE's digital twins\, supp
 orting uncertainty quantification and enhanced interactivity.\nThis course
  will introduce those working in meteorology and climate science to the co
 ncepts and methods of modern machine learning\, drawing on DestinE example
 s and applications and ECMWF's data-driven AIFS weather models. As there a
 re many general courses on machine learning available – including free o
 nline courses – this course will have a particular focus on the use of m
 achine learning in the domain of Earth System Sciences (ESS).\nMain topics
 \nThe course will cover the following themes:\n\nAn overview on the use of
  machine learning in ESS\nIntroduction into the most important machine lea
 rning methods that are relevant for ESS\, including deep learning approach
 es.\nExamples for the use of specific machine learning tools across the we
 ather and climate prediction workflow and how they can be prepared for use
  in operational predictions.\nData-driven forecasting\, based around AIFS 
 and the Anemoi framework\nAn overview of the DestinE programme and example
 s of how machine learning is being applied within DestinE.\n\nThe course w
 ill consist of lectures\, discussions\, and hands on sessions with code ex
 amples.\nRequirements\nParticipants should have a good meteorological or c
 limate-science background\, and a good familiarity with statistics. Partic
 ipants should also have some limited experience with Python code and Jupyt
 er notebooks. Basic experience with machine learning applications in Earth
  system sciences and the handling of Earth system data would be advantageo
 us. Some practical experience in numerical weather prediction is an advant
 age.\nAll lectures are in English\, and the training will take place at th
 e ECMWF office in Bonn\, Germany. As an EU-funded course\, priority will b
 e given to EU member states as well as ECMWF member states.\n\nhttps://eve
 nts.ecmwf.int/event/495/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/495/logo-3547453490.png
LOCATION:ECMWF 
URL:https://events.ecmwf.int/event/495/
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