Postdoctoral Researcher - Towards a high-fidelity Integrated Forecasting System via ground-breaking and ambitious data-assimilation of the dynamic soil-vegetation hydraulic continuum

To undertake research into ground-breaking and ambitious modelling and data assimilation (DA) of the representation of the dynamic soil-vegetation continuum within ECMWF’s land surface model and DA system, ECLand, and contribute to the overall goals of the AFESP programme, both in Theme 3 (“Data assimilation for the Earth System across a range of scales”) and across the three AFESP themes.

This role is funded by the research programme on Advancing the Frontiers of Earth System Prediction (AFESP) - a £30million 15-year investment by the University of Reading, in partnership with the European Centre for Medium-Range Forecasts (ECMWF), the UK Met Office and the National Centre for Atmospheric Science. It will deliver sustained investments to tackle some of the far-term (10–15 year) and difficult (high-risk, high-reward) research challenges in global Earth System prediction. 

By enhancing our capabilities in global data assimilation, simulation and analysis, the research programme will deliver a new class of accurate, reliable and usable forecasts, aiming to re-define the medium-range predictability limit from two to at least four weeks, enabling a wide range of new scientific and societal applications. 

Full time, fixed term post for 5 years.

Interview date 18/03/2024

Main duties and responsibilities 

Undertake collaborative research and make significant contributions to the following activities:  

• Research on processes and process chains that deliver to the ambition of increasing skill in NWP, with a focus on scales at which new observations and state-of-the art numerical simulations are more directly comparable.
• Develop and test new approaches in numerical simulation that target increased process fidelity, such as new parametrisations suitable for km-scale, including those based on alternative methods such as machine learning (ML). Prioritise use of unexplored observations, both for verification and as candidates for additional data assimilation.
• Research on developing new data assimilation techniques that are capable of improved land-atmosphere coupling, via dynamic adjustment of land surface parameters.
• Specifically, explore ML-based observation operators that inform the derivation of soil hydro-thermal and vegetation parameters in the ECLand DA system by exploiting satellite observations across a range of wavebands, including visible wavelengths, and active and passive microwave.
• Actively engage with the latest theory and modelling of the soil-plant hydraulic system, to work towards a unifying soil hydro-thermal theory that can be used, in conjunction with suitable remote sensing observables, to explore the use of a novel ‘vegetation as a root-zone soil sensor’ (VaaSS) approach for spatio-temporal derivation of subsurface properties in the ECMWF ECLand DA system.
• Develop, test and evaluate the ECLand DA system with updated soil and vegetation parameters, at a range of spatio-temporal scales.
• Produce and analyse subsequent medium-range coupled land-atmosphere NWP experiments to assess the impact of updated land parameters.
• Attend, contribute to, and organise relevant project meetings.

Information on further research posts on the working on the Advancing the Frontiers of Earth System Prediction (AFESP) research programme available here 

Informal contact details

Contact role:

Professor of Soil Physics and Micrometeorology, and PI of the project

Contact name: Professor Anne Verhoef

Contact email: [email protected]

Alternative informal contact details

Contact role:

Professor of Climate System Science & Climate Hazards and Director of University of Reading - ECMWF Research Collaboration

Contact name: Professor Pier Luigi Vidale

Contact email: [email protected]