Data Assimilation Scientist

at  ECMWF

ABOUT ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world-leader in weather and environmental forecasting. As an international organisation we serve our members and the wider community with global weather predictions and data that is critical for understanding and solving the climate crisis. We function as a 24/7 research and operational centre with a focus on medium and long-range predictions, holding one of the largest meteorological data archives in the world. The success of our activities builds on the talent of our scientists and experts, strong partnerships with 35 Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies and machine learning across our operations. ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/supercomputer in Bologna, Italy, and a large presence in Bonn, Germany.
ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme. Other areas of work include High Performance Computing (HPC) and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.
For additional detail about ECMWF, see www.ecmwf.int

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
  We encourage you to apply even if you don’t feel you meet precisely all these criteria.
  Candidates must be able to work effectively in English . A good knowledge of one of the Centre’s other working languages (French or German) is an advantage.

YOUR ROLE

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