Scientist for Ensemble Modelling

ECMWF; Reading; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

ECMWF is seeking a Scientist in the newly-formed Ensemble Modelling Team to advance operational ensemble prediction systems from medium-range to seasonal timescales. The role focuses on improving ensemble configurations, representing model and initial-condition uncertainty, and developing physics-based, AI-based and hybrid approaches.

The successful candidate will design experiments, test upgrades, and prepare training datasets, collaborating with data assimilation, forecast production and user

Qualifications

• Experience in Earth-system modelling and understanding of physical processes influencing predictions.
• Experience with stochastic methods used in probabilistic forecasting, including stochastic parametrisation schemes.
• Experience developing ensemble prediction systems for medium-range, sub-seasonal or seasonal timescales.
• Strong programming and data-analysis skills, experience in scientific software, HPC environments and robust configurations.

Responsibilities

• Enhance the representation of model uncertainty in IFS ensemble forecasts, including through the stochastically perturbed parametrisation scheme.
• Contribute to improving AIFS and nudged ensemble configurations, including through the preparation of training datasets and the design of calibrated ensemble systems.
• Optimise the initialization and representation of initial-condition uncertainty in ensemble forecasts across forecast lead times from days to seasons, including through singular vectors and use of initial conditions from the ensemble data assimilation system.
• Design numerical experiments, analyse results and implement robust code, while maintaining operational configurations and resolving emerging issues in the operational systems.
• Collaborate across ECMWF and with Member and Co-operating States to advance operational ensemble prediction systems, representing ECMWF where appropriate.

Skills

Analytical skills

Problem solving

Programming

Data analysis

Communication

Education

Advanced university degree

Job description

Your role

Ensemble prediction is central to ECMWF’s operational forecasts and products. Reliable probabilistic forecasts enable users in Member and Co-operating States and beyond to understand forecast uncertainty and make better-informed decisions.

We are seeking a Scientist in the newly-formed Ensemble Modelling Team to work on ECMWF’s operational ensemble prediction systems across medium-range to seasonal timescales. The role will contribute to improving and maintaining ensemble configurations used in operations, leveraging physics-based, AI-based and hybrid approaches.

You will work on ECMWF’s operational IFS ensemble forecasts, with a particular focus on the representation of model uncertainty through the stochastically perturbed parametrisations scheme. You will also contribute to ECMWF’s operational AIFS ensemble forecasting capability and to hybrid ensemble configurations, including nudged ensembles.

You will also work on the representation of initial-condition uncertainty for the operational ensemble systems, using singular vectors and initial conditions from the ensemble data assimilation system.

More broadly, the role will involve numerical experimentation, testing and implementation of ensemble system upgrades, supporting robust operational configurations, and preparing training datasets.

About the Ensemble Modelling Team

The Ensemble Modelling Team will be part of the Earth System Modelling Section in ECMWF’s Research Department. It will consolidate and advance ECMWF’s operational ensemble prediction systems across medium-range, sub-seasonal, seasonal and longer-range applications.

The team will be responsible for ensemble configuration design, initialisation, representation of model and initial-condition uncertainty, calibration and reliability. Its work will span physics-based, AI-based and hybrid systems, sharing methods across timescales where relevant and tailoring approaches to different prediction ranges where needed.

The team will also maintain, support and further advance ECMWF’s operational ensemble systems, using numerical experimentation to guide improvements and address issues that may arise in operations. It will work with colleagues across data assimilation, atmospheric, ocean and land modelling, machine learning, evaluation, forecast production and user services to support the delivery of world-leading ensemble predictions.

Your responsibilities

You will:

• Enhance the representation of model uncertainty in IFS ensemble forecasts, including through the stochastically perturbed parametrisation scheme
• Contribute to the improving AIFS and nudged ensemble configurations, including through the preparation of training datasets and the design of calibrated ensemble systems
• Optimise the initialisation and representation of initial-condition uncertainty in ensemble forecasts across forecast lead times from days to seasons, including through singular vectors and use of initial conditions from the ensemble data assimilation system
• Design numerical experiments, analyse results and implement robust code, while maintaining operational configurations and resolving emerging issues in the operational systems
• Collaborate across ECMWF and with Member and Co-operating States to advance operational ensemble prediction systems, representing ECMWF where appropriate

What we are looking for

We are looking for someone who combines strong scientific expertise relevant to the role with a practical approach to developing operational forecasting systems. You are interested in working across physics-based, AI-based and hybrid ensemble prediction.

You understand probabilistic methods, statistical techniques and physical processes, and have the technical expertise needed to develop stable and resilient operational model configurations.

You will bring:

• Excellent analytical and problem-solving skills, with the ability to connect theoretical understanding with practical model development
• A rigorous and proactive approach to improving forecasting methods and code
• An ability to work independently while collaborating effectively with colleagues across different areas, supported by clear communication skills
• Strong organizational skills, including the ability to manage competing priorities and deliver high-quality work when timelines are constrained
• A commitment to documenting methods, code and results

Your profile

Education

• An advanced university degree or equivalent professional experience, in a relevant field

Essential experience and knowledge

• Experience in Earth-system modelling, with a good understanding of physical processes which influence predictions on timescales from days to seasons ahead
• Experience with stochastic methods used in probabilistic forecasting, including stochastic parametrisation schemes
• Experience developing ensemble prediction systems for medium-range, sub-seasonal or seasonal timescales
• Strong programming and data-analysis skills, with experience developing scientific software, conducting large numerical experiments in high-performance computing environments, and maintaining robust model configurations

Desirable experience and knowledge

• Experience with AI-based ensemble forecasting systems
• Experience with relevant methods for representing initial-condition uncertainty

Languages

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.

At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.

About ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high-quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.

The success of our activities depends on the funding and partnerships of our 35 Member and Co-operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful supercomputing capabilities,are the core of a 24/7 research and operational centre with a focus on medium and long-range predictions. We also hold one of the largest meteorological data archives in the world.

Our vision: World-leading monitoring and predictions of the Earth system enabled by cutting-edge physical, computational and data science, resulting from a close collaboration between ECMWF and the members of the European Meteorological Infrastructure, will contribute to a safe and thriving society

Our mission: Deliver global numerical weather predictions focusing on the medium-range and monitoring of the Earth system to and with our Member States

In addition, ECMWF has established 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, as well as being a contributor to the Copernicus Emergency Management Service. Other areas of work include High Performance Computing 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 its headquartersin Reading, UK, a data centre in Bologna, Italy, and a large presence in Bonn, Germany as a central location for our EU-related activities.ECMWF is internationally recognised as the voice of expertise in numerical weather predictions for forecasts and climate science.

www.ecmwf.int

About Copernicus copernicus.eu

About Destination Earth destine.ecmwf.int

Other information

Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. In addition to basic salary, ECMWF also offers an attractive benefits package. Information about working with us and full details of salary scales and allowances are available on the ECMWF website at: www.ecmwf.int/en/about/jobs/working-ecmwf

Starting date: asap

Location: Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)

Remote work: As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking. We allow for remote work 10 days/month away from the office, including up to 80 days/year away from the duty station country (within the area of our member states and co-operating states).

Interviews by videoconference (MS Teams) interviews will be conducted in English and are expected to take place approximately a month after the closing date.

Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.

If you require any special accommodations in order to participate fully in our recruitment process, please let us know. To contact the ECMWF Recruitment Team, please email [e-mail].

Who can apply

Applications are invited from nationals from ECMWF Member States and Co-operating States.

ECMWF Member and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.

In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy (note: ECMWF will not be able to assist with leaving the country). Applications from nationals from other countries may be considered in exceptional cases.