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The scientist recruited for this role will be responsible for adapting and deploying state-of-the-art data assimilation methodologies used in the NWP workflow towards the specific needs and requirements of future reanalysis systems. Concurrently, he/she will take the lead in developing bespoke solutions for reanalysis data assimilation when these methods are not yet mature.
A specific focus of the role is towards the development, adaptation and extension of the ECMWF variational and ensemble-variational DA systems to increase their skill and reduce their computational costs when deployed in the future C3S reanalysis framework. The objective is to better exploit the capabilities of 4D-Var and the ECMWF Ensemble of Data Assimilations (EDA) system to achieve a step change in the accuracy and fidelity of future reanalysis products while reducing overall computational costs. This development work will take place using both established variational/optimal estimation technologies and emerging machine learning methodologies.
Together with algorithmic developments, the role involves coding them into the ECMWF Integrated Forecasting System on a High Performance Parallel Computing infrastructure. The successful candidate will embrace the technical complexities of the job and be alert to the opportunities of the rapidly evolving computing infrastructure.
The scientists will be based in the Data Assimilation Methodologies team within the ESAS Section and will work in close collaboration with colleagues from the C3S Reanalysis Team.
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