Scientist Uncertainty Quantification for Destination Earth Reading 2023

ECMWF has an exciting opportunity for a Scientist (A2) to help shape and deliver Destination Earth (DestinE) developments, by making use of machine learning techniques and statistical methods. You will apply powerful data processing techniques based on machine learning and statistical methods to support the uncertainty quantification for the Digital Twin for weather induced extremes. This Digital Twin will rely on high-resolution (km-scale) simulations produced with ECMWF’s Integrated Forecasting System (IFS) to drive much enhanced weather-induced extremes predictions.

The role

Uncertainty quantification is particularly essential for the prediction of extreme events, where it is important to know whether there is even a slight chance of an event occurring. Operationally, most weather centres (including ECMWF) run ensemble forecasts generating an array of equally likely forecasts, given assumptions about the uncertainty in the observations and models. However, for the resolutions and data volumes envisaged in DestinE, running large ensembles in a timely manner will be very difficult. Machine learning offers new levels of complex data processing that can enhance the representation of uncertainty and complement ensemble methods by, for example, blending ensemble members from different physical and data-driven models or generating new members.

You will join an existing group at ECMWF working on applying machine learning and statistical techniques to improve various aspects of the ECMWF modelling and operational workflows, and will make the link between these efforts and similar efforts in DestinE. You will also contribute to regular progress reports to the European Commission.

Your responsibilities

• Apply machine learning models and techniques to support uncertainty quantification for the weather-induced extremes Digital Twin
• Develop verification techniques to evaluate the accuracy and physical and spatial consistency of uncertainty information in generated forecasts
• Develop and test complex workflows in advanced digital technology environments on some of the largest computing and data handling infrastructures in Europe
• Contribute to regular progress reports to the European Commission and supporting procured activities within DestinE

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. We appreciate the need for flexibility in the way our staff work. We adopted 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.

ECMWF has developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Climate Change and Atmosphere Monitoring Services of the EU Copernicus Programme. We also contribute 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 also one of the three entities entrusted to implement the DestinE initiative of the European Commission, alongside with ESA and EUMETSAT as partners. DestinE aims to deploy several highly accurate thematic digital replicas of the Earth, called Digital Twins. The Digital Twins will help monitor and predict environmental change and human impact, in order to develop and test scenarios that would support sustainable development and corresponding European policies for the Green Deal.  ECMWF is responsible for the delivery of these digital twins and of the Digital Twin engine, the software infrastructure needed to power them of some of Europe’s largest supercomputers, those of the European HPC Joint Undertaking (EuroHPC). The first phase of DestinE covers the period December 2021 – June 2024, and future phases are foreseen (subject to funding).

What we’re looking for

• Excellent analytical and problem-solving skills with a proactive and constructive approach.
• Flexibility, with the ability to adapt to changing priorities.
• Initiative and ability to work collaboratively with other ECMWF staff and European DestinE partners, but also able to work independently.
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines and in a matrix managed environment.

Education Knowledge Skills Experience

• Advanced level degree (EQF Level 7 or above) in Earth System Science, Physics, Applied Mathematics, Computer Science, or a related discipline.
• Experience using Python (or similar programming languages) to interact with large datasets.
• Demonstrable experience in the use of machine learning in applications within Earth system science.
• Knowledge of deep learning frameworks (e.g., Pytorch, Tensorflow)
• Experience with generative modelling using GANs, VAEs or Diffusion approaches would be desirable.
• Some knowledge of meteorology and ensemble methods would be advantageous.
• Some experience with communicating scientific results to a general audience and the writing of scientific reports would be beneficial.
• 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.

Other information

Grade remuneration: The successful candidate will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. The annual basic salary will be GBP 68.374 NET of tax (Duty Station Reading, UK) or EUR 83.888 NET of tax (Duty Station Bonn, Germany). ECMWF salaries are exempt of national income tax. On top, ECMWF also offers a generous benefits package and a flexible teleworking policy. The position is assigned to the employment category STF-PS as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at www.ecmwf.int/en/about/jobs, including the ECMWF Staff Regulations and the terms and conditions of employment.