I join ECMWF with the role of land surface modeller actively contributing 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. I hold key responsibilities for the hydrological forecasting chains at ECMWF. The model development is to 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 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, use of the emerging techniques with the purpose of model enhancements is foreseen.

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.

Specific responsibilities include:

• 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.