Job summary

We are looking for a highly motivated hydrologist or land surface modeller to join our growing community of scientists working on the enhancement of the hydrological processes, including the soil-vegetation-atmosphere interactions, within ECMWF’s land surface model (ecLand) and to assess the impact on the hydrological and atmospheric forecasts when compared against global flux and streamflow observations.

In your role you will actively lead and contribute to the delivery and implementation of changes to ecLand’s land surface processes. This work is directly linked to the development of the next generation of hydrological forecasting and numerical weather prediction systems within the centre’s operational modelling chain, through the modification and uplift of the hydrological forecasting capabilities of the underlying land surface model. The successful candidate will therefore hold key responsibilities for the hydrological forecasting chains at ECMWF.

This development will be based on the revision of existing surface and subsurface runoff-generating processes within ecLand, and the integration of shallow surface water to better simulate inundation. The successful candidate will also contribute to the calibration of existing and/or the incorporation of additional processes in the model, such as evaporation and transpiration, or alternative modelling strategies with the overall aim of improving the Centre’s forecast skill. Given the current development of ML/AI forecasting systems at the Centre, an interest in the use of this emerging field for the purpose of model enhancements is expected.

Specific considerations include:

• Revision of the runoff generating processes within ecLand, in particular:
  • the runoff- and streamflow-generating processes across diverse ecosystems,
  • the interaction of surface water with the land surface and the atmosphere, following inundation or irrigation,
• Improving hydrological and general forecasting skills in observational data-poor regions.

Collaboration is a cornerstone of this role. The candidate is expected to develop close working relationships with teams within the Research and the Forecast and Services Department at ECMWF, and its main collaborators.

Your responsibilities

• Diagnose model performance and physical consistency to improve the hydrological processes within ecLand, and subsequently assess the impact of those changes on the operational IFS.
• Take responsibility to enhance the hydrological performance and forecasting skills through regular major hydrological modelling system upgrades (e.g. calibration, review of forcing data, update of modelling framework).
• Ensure that modifications are actively reviewed and evaluated against available observations.
• Maintenance of developed code within ecLand and support code integration into the operational IFS.
• Work with ML scientists on leveraging data-driven approaches for the improvement of the land surface model.

What we’re looking for

• Excellent analytical and problem-solving skills with a proactive, independent approach.
• Positive attitude towards working in an international operational environment.
• Flexibility, with the ability to adapt to changing priorities and project needs, in particular with the advent of ML/AI in the field.
• Good interpersonal and communication skills.
• Dedication, passion, and enthusiasm to succeed both individually and across teams of different discipline.

Education

• A doctoral university degree (EQ8 level) or substantial equivalent professional experience in hydrology or a related discipline.

Experience, Knowledge and Skills

• Extensive experience in modelling land surface processes, in particular related to the water cycle, including the assessment and revision of model processes and parameters.
• Relevant experience in writing reports, scientific papers or other relevant documentation.
• Experience in processing and utilising observational datasets, such as river discharge, for model validation purposes.
• Experience in processing output from numerical weather prediction (NWP) models would be an advantage.
• Experience in the use of remotely sensed data would be an advantage.
• Experience in the use of ML in hydrological modelling would be an advantage.
• Excellent knowledge of scientific programming, programming language and shell scripts. Knowledge of Fortran and/or Python would be an advantage.
• Good knowledge of Linux systems (and/ or UNIX).
• The experience of working in an operational framework would be an advantage.
• Candidates must be able to work effectively in English and interviews will be conducted in English.
• Good knowledge of one of the Centre’s other working languages (French or German) would be an advantage.

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