The Role

We are looking for a highly motivated (Senior) Scientist to work on improving the representation of convective processes within the global operational numerical weather prediction system at ECMWF (the Integrated Forecasting System, IFS). The successful candidate will develop the physical parametrisation of convection (both shallow and deep) across timescales (from days to decades) and resolutions (from fully parametrized at coarse resolution to partially resolved deep convection at km-scale). A particular focus is on the prediction of severe weather from convective storms as well as the large-scale impacts of convection on the atmospheric circulation, including tropical dynamics such as the Hadley circulation, monsoons and the Madden-Julian Oscillation (MJO). Other challenges include improving the interactions with the dynamics, turbulence and cloud parametrizations, quantitative precipitation forecasting, convective organisation and the diurnal cycle of convection. The role will involve modification of the IFS code, performing model simulations, evaluating impacts using a range of observations and metrics, developing diagnostics and implementing improvements in operational IFS upgrades. The successful candidate will also advise on developments of ECMWF’s machine learning forecast system (the Artificial Intelligence Forecast System, AIFS) regarding the representation of convective processes and precipitation.

As the process of convection is closely linked to many processes within the Earth system, improvements and developments in the representation of convective processes will be performed and evaluated in close collaboration with others in the atmospheric physical processes team and the Destination Earth initiative working on turbulence, clouds and radiation, as well as other teams in the Earth system modelling section and across ECMWF working on dynamics, land/ocean-atmosphere coupling, representing uncertainty, data assimilation and forecast evaluation.

The Team

This position is based in the Physical Processes Team, responsible for the improved representation of atmospheric processes within the IFS. The team is part of the Earth System Modelling Section of the Research Department.

At ECMWF, you will find a passionate community, collectively aiming to build world-leading global Earth system models for numerical weather prediction. This effort supports ECMWF’s strategy of producing cutting‐edge science and world-leading weather predictions and monitoring of the Earth system.

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 has 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 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 headquarters in Reading, UK, a data centre/ supercomputer in Bologna, Italy, and a large presence in Bonn, Germany. See www.ecmwf.int for more info about what we do.

Main duties and key responsibilities

  • Maintain, develop and improve the representation of convection (both shallow and deep) in ECMWF’s forecast systems across timescales (from days to decades) and resolutions (up to km-scale)
  • Perform coupled (ensemble) forecasts with the IFS and evaluate using a range of observations and metrics to identify systematic errors and assess improvements for operational Cycle upgrades
  • Collaborate with other scientists at ECMWF on the interactions of convective processes with the atmospheric dynamics and all other physical processes in the Earth system across scales (including partially resolved convection at the km-scale)
  • Assess the impact of convective processes on other aspects of the Earth system, including tropical dynamics such as the Hadley circulation, monsoons and the MJO
  • Collaborate with developers and users of the IFS convection code in the Member States and beyond
  • Collaborate with machine learning scientists on the representation of convection in the AIFS and assess potential hybrid physical model-ML developments
  • Contribute to the maintenance and development of collaborative software tools for evaluating model physics and parametrisation developments, and respond to model-related issues in the operational forecast system as required

    What we are looking for

    • Excellent analytical and problem-solving skills with a proactive approach to improve models and tools
    • Excellent interpersonal and communication skills
    • Self-motivated and able to work with minimal supervision as well as collectively as part of a team
    • Dedication, passion and enthusiasm to succeed both individually and collaboratively
    • Ability to maintain effective communication and documentation of scientific results
    • Highly organised with the capacity to work on a diverse range of tasks to tight deadlines

      Education

      • Advanced university degree (EQ7 level or above) in a physical, mathematical or environmental science, or equivalent professional experience

        Experience, knowledge and skills

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