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.

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

About Copernicus

Copernicus is the earth observation component of the European Union (EU) space programme. Based on the exploitation of spaced based and in situ (earth-based) observations and scientific models, Copernicus provides information services for land, marine, atmospheric and climate monitoring, as well as emergency management and security. These services, and their free, open and quality assured data and tools, support a range of environmental and security applications across sectors and policy domains. For details, see www.copernicus.eu

The Copernicus Atmosphere Monitoring Service (CAMS) service provides consistent and quality-controlled information related to air pollution and health, solar energy, greenhouse gases and climate forcing, everywhere in the world. For details, see https://atmosphere.copernicus.eu

The Copernicus Climate Change Service (C3S) service provides authoritative information about the past, present and future climate, as well as tools to enable climate change mitigation and adaptation strategies by policy makers and businesses. For details, see https://climate.copernicus.eu

About Climate Reanalysis

Climate Reanalysis is part of the ECMWF Research Department’s activities and is funded by C3S. We develop, maintain and safeguard the quality of state-of-the-art global climate reanalysis. In collaboration with C3S contractors, we also oversee the collection of historical satellite and in-situ observations that, for example, feed into future reanalysis systems and the production of regional reanalysis for the European domain.

The production of global climate reanalysis is an important activity at ECMWF with a long history. Reanalysis is an integrator of all available historical observations and transforms these into as-accurate-as-possible and easy to use global ‘maps without gaps’ of our recent climate. The latest state-of-the-art ERA5 reanalysis is an ECMWF and C3S flagship product which provides hourly global fields from 1940 onwards for many quantities of the atmosphere, land surface and ocean surface. It is highly popular and serves over 170,000 users via the Climate Data Store for a wide range of applications and is highly cited in the scientific literature. ERA5 is based on the same data assimilation methodology as applied in the initialisation of medium-range weather predictions, where observations are mixed with a model first guess to produce an optimal (re)analysis. We maintain ERA5 close to real time in an operational fashion. We are also preparing for ERA6, the next generation reanalysis, which will have a one-way coupling with the ocean component. Future reanalysis is aimed progressively towards fully coupled Earth system data assimilation.

This post will be based in ECMWF’s Research Department (RD) and will work both with Integrated Forecast Systems (IFS) Section and the Climate Reanalysis Team (CRT). The IFS Section is responsible for the technical development, maintenance and support of the software which enables ECMWF to maintain its world leading status in weather and environmental forecasting. The CRT is responsible for the development and quality assurance of ECMWF global atmospheric reanalysis.

For additional details, see climate.copernicus.eu

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

What we are looking for

• Scientific software engineer with experience dealing with and analysing large, complex datasets
• Experience of working in a collaborative scientific research environment
• Strong track record of developing high quality software to support scientific research
• Excellent analytical and problem-solving skills with a proactive and constructive approach
• Flexibility, with the ability to adapt to changing priorities.
• Ability to work autonomously and as part of multidisciplinary and geographically distributed teams
• Excellent interpersonal and communication skills
• Enthusiastic to teamwork and to closely interact with colleagues across the organization
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines.

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.

We encourage you to apply even if you don’t feel you meet precisely all these criteria.