Postdoctoral Researcher – UrbaNE: Urban Numerical weather prediction Expedited

To undertake research within the URBANE (Urban Numerical Weather Prediction Expedited) project.

This role is funded by the research programme on Advancing the Frontiers of Earth System Prediction (AFESP) - a £30 million 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. AFESP 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. URBANE contributes to the overall goals of the AFESP programme, primarily in the theme of Challenges and opportunities in simulating the Earth System at the kilometre-scale. 

By enhancing our capabilities in global data assimilation, simulation and analysis, AFESP 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. Within that, the URBANE project focuses on services and forecasts for people and infrastructure in urban environments. This is important to improve services that support health, infrastructure risk, and climate-change resilience. The project will support both ECMWF and Met Office in their drives towards representing urban environments better and at appropriate scales within numerical weather prediction (NWP). The key contribution will be evaluating urban NWP with observations, particularly taking better advantage of high resolution thermal remote sensing (RS), field campaign data and radiative transfer modelling. 

Full time, fixed term post for 5 years

Interview date W/C18/03/2024

Main duties and responsibilities 

Working with ECMWF, Met Office, NCEO (National Centre for Earth Observation), ERC urbisphere and NERC ASSURE researchers the candidate will:  

• Scope and gather the best data from RS and ground-based data sources, considering site characteristics (e.g. urban form) and resolution. 
• Develop detailed understanding of what NWP model state variables (e.g. temperatures) & parameters (e.g. emissivities) represent in space and time in comparison to observed quantities. 
• Prepare, quality-assure and preprocess observations for use in model evaluation; including source area/field of view analysis.
• Work with radiative transfer models of high and intermediate complexity (DART and SPARTACUS-surface) and develop modules for simple representation of urban surfaces in terms of NWP variables/parameters for the fast model RTTOV.
• With support from NWP-centre collaborators (i) assess NWP model components to be evaluated against observations and (ii) coordinate NWP model runs (e.g. IFS, UM100) matched to observational campaigns/scenes 
• Undertake model – observation comparisons using prepared observations and the full hierarchy of radiative transfer models across different scales (localised to cities globally), interpret the evaluation results, identify implications and suggest improvements (model, observation preparation).
• Propose improvements for models and where appropriate contribute to the NWP-centre led developments. 
• Publish high quality peer review papers, give conference presentations, prepare articles in the international community newsletters (e.g. IAUC newsletters); participate in institutional and cross-projects workshops 

• 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 the extended NWP range, with a focus on scales at which new observations and state-of-the art numerical simulation are more directly comparable. 
• Develop and test new approaches in numerical simulation that target increased process fidelity, including enhanced resolution, new parametrisations suitable for km-scale, including alternative methods such as machine learning. 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 handling dense data, as well as increased coupling between Earth System components 
• Attend, contribute to, and organise relevant project meetings. 
• Report on progress and results of the research through appropriate methods, including papers for submission to scientific journals, presentation of results at conferences and workshops, etc.
• Maintain awareness of current progress in relevant research areas to ensure that the research remains at the cutting edge. 
• Liaise with collaborators from other parts of the programme on a regular basis, including travel to visit project partners as required. 

Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. By reference to the applicable SOC code for this role, sponsorship may be possible under the Skilled Worker Route.  Applicants should ensure that they are able to meet the points requirement under the PBS.  There is further information about this on the UK Visas and Immigration Website.

The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and champions LGBT+ equality. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs.