Scientific Software Engineer

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.
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. We appreciate the need for flexibility in the way our staff work. Weadopted a hybrid work model that is widely used by staff across ECMWF - allowing everyone to work in the office working as well as remotely up to 10 days/month, including away from the duty station.
For additional detail about ECMWF, see www.ecmwf.int

EDUCATION

• Completion or close to completion of a PhD in a field of science requiring a strong component of numerical methods, or equivalent career experience.

EXPERIENCE, KNOWLEDGE AND SKILLS

• Excellent knowledge and working experience with Python, Fortran and UNIX scripting is essential
• Working experience with other high-level programming languages such as C or C++ is an advantage
• Experience with meteorological or climate data and observation handling or data assimilation is a strong advantage
• Experience with machine learning algorithms is an advantage
• Scientific approach and mindset
• Candidates must be able to work effectively in English. Knowledge of one of ECMWF’s other working languages (French or German) would be an advantage.

THE ROLE

An important aspect of operational climate reanalysis is the quality assurance of its products prior to publication. For this purpose, we closely monitor the quality of ingested observations, and the resulting gridded products produced by ERA5 (and soon ERA6). This is achieved using a detailed set of diagnostics that are produced and updated daily in an automatic suite of jobs running on our HPC system.
The successful applicant will be responsible for the maintenance of this set of tools, resolving any emerging issues and adapting the system in a timely manner as required. This involves both algorithms (mostly coded in Fortran and Python) and the suites that schedule and run the code daily (mostly Python and UNIX shell scripts). This will also include the design and implementation of new diagnostics and the exploration of the use of machine learning techniques.
Another element of the post’s responsibilities includes support and maintenance of the production of reanalysis systems and products in an evolving HPC environment. This will include the extended range of products that climate reanalysis produces, such as pre-calculated statistics (e.g. daily, and monthly means) from hourly global fields.
We will expect that you approach your role demonstrating established scientific and technical principles and practices and to develop a keen interest in reanalysis activities in general.

MAIN DUTIES AND RESPONSIBILITIES

Maintain and develop the technical and numerical infrastructure of the reanalysis monitoring system:

• Improvements to and responsive maintenance of the monitoring suite and its components
• Design and implementation of new monitoring tools
• Explore the usage of machine learning techniques

Maintenance of reanalysis-specific products:

• Pre-calculated statistics, additional parameters
• Compliance to and implementation of their evolving data formats and standards (e.g., GRIB)
• Optimization of their computational throughput and HPC footprint
• Employ established ECMWF working practices for the transfer of research developments into operations (R2O)

Maintenance of the technical reanalysis infrastructure in an evolving HPC environment, ensuring optimum use of ECMWF resources