07/09/2026
๐๐ ๐๐ผ๐๐ฟ๐๐ฒ ๐ฎ ๐ผ๐ณ ๐๐ต๐ฒ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด (๐ ๐) ๐ณ๐ผ๐ฟ ๐๐ฎ๐ฟ๐๐ต ๐ฆ๐๐๐๐ฒ๐บ๐ ๐ ๐ผ๐ฑ๐ฒ๐น๐น๐ถ๐ป๐ด ๐๐ฒ๐ฟ๐ถ๐ฒ๐ ๐ต๐ฎ๐ ๐ฐ๐ผ๐ป๐ฐ๐น๐๐ฑ๐ฒ๐ฑ ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐ด๐ถ๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ถ๐ ๐ป๐ผ๐ ๐ผ๐ฝ๐ฒ๐ป ๐ณ๐ผ๐ฟ ๐๐ผ๐๐ฟ๐๐ฒ ๐ฏ.
Developed within the framework of the European Commissionโs DG CNECT Destination Earth (DestinE) initiative, course 2 combined theory with hands-on exercises to explore the methods behind modern AI weather prediction systems.
Participants worked directly with AI weather models and explored tools including ECMWFโs ๐๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐ฆ๐๐๐๐ฒ๐บ (๐๐๐๐ฆ) and ๐๐ป๐ฒ๐บ๐ผ๐ถ, the open-source ML framework co- co-developed by ECMWF and several national meteorological services in its Member and Co-operating States.
The series now moves into its third and final course, ๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ฎ๐ป๐ฑ ๐๐๐๐๐ฟ๐ฒ ๐๐ถ๐ฟ๐ฒ๐ฐ๐๐ถ๐ผ๐ป๐, starting on ๐ฑ ๐ข๐ฐ๐๐ผ๐ฏ๐ฒ๐ฟ ๐ฎ๐ฌ๐ฎ๐ฒ.
Course 3 will explore how ML is being applied to areas including extreme weather, coupled Earth system modelling, data assimilation and sub-seasonal and long-range prediction
โก๏ธ Read about Course 2 and what comes next:
https://destine.ecmwf.int/news/machine-learning-for-earth-systems-modelling-series-moves-into-its-final-course/