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Postdoctoral Researcher - Isolating and Understanding the Most Predictable Components and Improving Their Representation in S2S Prediction

University of Reading - School of Mathematical, Physical and Computational Sciences

Location: Reading
Salary: £33,966 to £44,263 Grade 6, per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 26th January 2024
Closes: 26th February 2024
Job Ref: SRF46552
 

To undertake research isolating and understanding the most predictable components of the atmosphere and improving their representation in sub-seasonal prediction. The project takes a particular approach to the diagnosis of global analysis and S2S forecast data which is motivated by theory for the dynamics of large-scale atmospheric wave activity. We will identify mode-like structures that dominate observed variability in analyses and models, where each mode has an intrinsic frequency predicted from its spatial structure. We will explore the link between these intrinsic properties, long-term evolution, atmospheric predictability and the ability of models to make sub-seasonal predictions. The project contributes to the overall goals of the AFESP programme, both in the challenge “predicting the Earth System up to the sub-seasonal range” 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 w/c 18/03/2024

Main duties and responsibilities

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

  • Research centred on large-scale atmospheric dynamics, the intrinsic properties of modal dynamics and nonlinear interaction, natural and forced variability, diagnosis of global models and research leading towards the improvement of sub-seasonal prediction.
  • The research activity will involve application of theory in the implementation of numerical diagnostics extracting the background state and mode-like elements of variability. The methods will be applied to re-analysis and S2S ensemble forecast data from ECMWF and the Met Office. The aim is to identify the aspects of models that differ and dominate model and forecast error into the extended range of numerical weather prediction.
  • The postholder will be responsible for conducting the calculations and also the implementation of experimental runs with the ECMWF and Met Office models to test hypotheses related to reasons for differing behaviour and long-time evolution, working with the co-investigators.
  • 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.

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 in Atmospheric Dynamics

Contact name: Professor John Methven

Contact email: j.methven@reading.ac.uk

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: p.l.vidale@reading.ac.uk

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