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SUMMARY:Online course: ML for Earth Systems Modelling - Applications and F
 uture Directions
DTSTART:20261005T070000Z
DTEND:20261204T154500Z
DTSTAMP:20260827T143600Z
UID:indico-event-583@events.ecmwf.int
CONTACT:training@ecmwf.int
DESCRIPTION:\nDiscover how machine learning is being applied to real-world
  Earth system challenges and explore emerging approaches shaping the futur
 e of weather and climate modelling.\nThis is the third and final course in
  the Destination Earth (DestinE) Machine Learning for Earth Systems Modell
 ing training series developed by ECMWF. Building on the foundations and te
 chnical skills introduced in Courses 1 and 2\, Course 3 focuses on advance
 d applications of machine learning across weather\, climate and Earth syst
 em science.\nParticipants will explore how ML methods can be applied to ex
 treme events\, downscaling\, coupled Earth systems\, data assimilation and
  long-range prediction\, while also examining explainability and trust\, f
 oundation and hybrid models\, operational workflows and data-driven scient
 ific discovery.\nThe course has a strong practical emphasis\, combining ex
 pert lectures and talks with Jupyter notebooks using real-world workflows\
 , Python scripts\, demonstrations\, readings\, quizzes and expert discussi
 ons. It can be followed as the final step in the\ncomplete three-course le
 arning pathway or as standalone advanced modules by experienced practition
 ers with the required technical background.\nMain topics\nThe course cover
 s:\n\nML workflows for extreme events\nExplainability\, trust and model fa
 ilure\nFoundation models\nHybrid modelling\nSub-seasonal and long-range pr
 ediction\nDownscaling\nCoupled Earth systems\nML-based data assimilation\n
 End-to-end modelling\nForecast in a Box\nData-driven scientific discovery\
 nCommunity reflection\n\nTarget audience\nCourse 3 is an advanced technica
 l course aimed primarily at:\n\nResearchers and developers in weather\, cl
 imate and Earth system science\nOperational numerical weather prediction (
 NWP) and climate model developers\nML researchers working with environment
 al or geophysical data\nAdvanced PhD students and postdoctoral researchers
 \n\nRequirements\nParticipants are expected to have:\n\nFamiliarity with P
 ython and Python-based ML ecosystems\nExperience with NumPy/xarray and PyT
 orch or JAX\nAn understanding of the fundamentals of numerical weather pre
 diction and Earth system modelling\nKnowledge of core ML methods covered i
 n Courses 1 and 2\n\n \nPrior completion of Course 1 – Foundations and
  New Frontiers and Course 2 – Architectures\, Data\, and Prediction is r
 ecommended for learners who do not already have equivalent knowledge and e
 xperience.\nRegister now!\nRegister for this course through our eLearning 
 portal.\n\nhttps://events.ecmwf.int/event/583/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/583/logo-1447777313.png
LOCATION:Online
URL:https://events.ecmwf.int/event/583/
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