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SUMMARY:Training course: Machine learning for weather prediction
DTSTART:20240318T083000Z
DTEND:20240322T170000Z
DTSTAMP:20260715T230000Z
UID:indico-event-377@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:This four-day course focuses on machine learning for numerical
  weather prediction (NWP). This will include:an overview on the use of mac
 hine learning in Earth Sciences\,the introduction into the most important 
 machine learning methods that are relevant for Earth Sciences\,the introdu
 ction into software and hardware frameworks at ECMWF to facilitate the use
  of machine learning\,and examples for the use of specific machine learnin
 g tools across the weather and climate prediction workflow and how they ca
 n be prepared for use in operational predictions.As 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.  As well as lectures there are discuss
 ions and hands-on sessions.Main topics    An overview on the use of mac
 hine learning in Earth Sciences    Software for machine learning in NWP
     Hardware for machine learning in NWP    Datasets that are availa
 ble and data retrieval    Regression and decision trees    Deep lear
 ning    Hybrid modelling    Introduction to data-driven forecasting
     Examples of machine learning applicationsRequirementsParticipants s
 hould have a good meteorological or a good machine learning background\, o
 r both. Participants should also have some limited experience with Python 
 code and Jupyter notebooks. Basic experience with machine learning applica
 tions in Earth system sciences and the handling of Earth system data would
  be advantageous. Some practical experience in numerical weather predictio
 n is an advantage.All lectures are in English.\n\nhttps://events.ecmwf.int
 /event/377/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/377/logo-3547453490.png
LOCATION:ECMWF 
URL:https://events.ecmwf.int/event/377/
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