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SUMMARY:Training course: Data assimilation & Machine Learning
DTSTART:20270111T084500Z
DTEND:20270115T143000Z
DTSTAMP:20260719T005600Z
UID:indico-event-578@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:\nThis 5-day training course focuses on describing Data Assimi
 lation (DA) methods and general aspects of assimilating observations. Aspe
 cts of the practical implementation of the assimilation techniques for rea
 l-size numerical weather prediction (NWP) systems will also be described.\
 nA new focus of the course is describing 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\n \nRequirements\nParticipants should have a good meteorologi
 cal and mathematical background\, and in particular a good understanding o
 f linear algebra. They are expected to be familiar with the contents of st
 andard meteorological and mathematical textbooks.\nIntroductory material n
 ot covered by the course can be found in our lecture note series.\nSome pr
 actical experience in numerical weather prediction is an advantage.\nThis 
 training is in-person\, and all lectures will be given in English.\n\nhttp
 s://events.ecmwf.int/event/578/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/578/logo-3547453490.png
LOCATION:ECMWF
URL:https://events.ecmwf.int/event/578/
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