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SUMMARY:Training course: Data assimilation & Machine Learning
DTSTART:20250317T084500Z
DTEND:20250321T143000Z
DTSTAMP:20260715T232600Z
UID:indico-event-436@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:\nThis five-day course focuses on describing Data Assimilation
  (DA) methods and general aspects of assimilating observations. Aspects of
  the practical implementation of the assimilation techniques for real-size
  numerical weather prediction (NWP) systems will also be described.\nA new
  focus of the course will be to describe how Machine Learning techniques a
 re being incorporated in the traditional DA workflow\, and discuss what ad
 vantages they can bring in terms of both performance and efficiency.\nAs w
 ell as lectures\, there will be discussion and hands-on sessions. \nMain 
 topics\n\nThe fundamental data assimilation concepts\nOptimal Interpolatio
 n\, 3D-Var\, 4D-Var and the Kalman filter\nEnsemble Kalman Filter methods\
 ; Ensemble of Data Assimilations and uncertainty estimation\; Hybrid varia
 tional/ensemble based methods\nModelling of error covariances\; handling o
 f non-Gaussian errors\nMachine Learning for DA: model error estimation and
  correction\, generative AI applications\, hybrid modelling\nThe global ob
 serving system\, with emphasis on how to use satellite observations\nBias 
 correction\, quality control and diagnostics\nApplications of data assimil
 ation methods for the land surface\, ocean\, atmospheric composition and r
 eanalysis\n\nRequirements\nParticipants should have a good meteorological 
 and mathematical background\, and in particular a good understanding of li
 near algebra. They are expected to be familiar with the contents of standa
 rd meteorological and mathematical textbooks.\nIntroductory material not c
 overed by the course can be found in our lecture note series.\nSome practi
 cal experience in numerical weather prediction is an advantage.\nAll lectu
 res are given in English.\nFeedback survey: https://emea.dcv.ms/FGVliMqMYu
 \n\nhttps://events.ecmwf.int/event/436/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/436/logo-3547453490.png
LOCATION:ECMWF
URL:https://events.ecmwf.int/event/436/
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