Job Summary: 

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.

Main duties and key responsibilities:

• Develop and implement scientific and technical innovations in the ECMWF 4D-Var – based assimilation system and its EDA component to improve reanalysis accuracy and fidelity
• Further develop and improve methodologies for uncertainty estimation and modelling in the assimilation cycle, including Machine Learning solutions
• Explore and develop innovative solutions for the improved representation of large scale circulation properties and constraints which are important for the identification of climate trends
• Contribute to the maintenance and support of the DA and ensemble DA systems in reanalysis

Qualifications:

• Excellent interpersonal and communication skills
• Strong analytical problem-solving and scientific curiosity
• Highly motivated to inspire scientific and technical innovation
• Dedication and enthusiasm to lead and work in a team
• Ability to work efficiently and complete a diverse range of tasks in a timely manner

Education:

• A university degree (EQF Level 8) or equivalent industry experience

Experience required in the following areas:

• Experience of development of data assimilation systems for Numerical Weather Prediction or other environmental applications is essential
• Experience of scientific software development on High Performance Computing systems is desirable
• Experience in the use of Machine Learning technologies is desirable

Knowledge and skills required:

• In depth understanding of data assimilation methodologies and techniques
• General knowledge of meteorology and/or climatology and/or Earth System science
• Proficiency in scientific computing (Fortran, C++, python, code and workflow management systems)
• Knowledge and experience in developing machine learning applications would be a plus
• Scientific planning, reporting and communication (written and verbal)
• Candidates must be able to work effectively in English and interviews will be conducted in English