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The role involves leading a small team of scientists working to improve understanding of earth-system sub-seasonal forecast performance, processes and predictability, and to drive improvements in ECMWF forecast performance
Work on the representation of uncertainty in ECMWF’s ensemble forecasts of the Integrated Forecasting System (IFS) as well as the maintenance of the tangent linear (TL) and adjoint (AD) code for the IFS physical parametrisations used during the
You will bring in the scientific and technical expertise into SEWA and ArcX-Climate Change Resilience on the use of AI/ML in sub-seasonal to seasonal forecasts time scales, in coordination with scientists and operators at African regional climate Centres