PhD Studentship in Mathematics: Randomized parallel algorithms for data assimilation in numerical weather prediction.

PhD Studentship in Mathematics

Project title:  Randomized parallel algorithms for data assimilation in numerical weather prediction.

Department/School: Department of Mathematics & Statistics

Supervisors:  Jennifer Scott (UoR), Amos Lawless (UoR), Massimo Bonavita (ECMWF), Nicolas Bousserez (ECMWF).

Project Overview: 

This is an exciting opportunity to work on the mathematics and computation of numerical weather prediction. You will be based at the University of Reading and will work with the European Centre for Medium-range Weather Forecasts (ECMWF). Data assimilation is a mathematical technique used to update a computer model forecast using the latest observational data. It is important in many application areas and, in particular, it is a fundamental part of modern-day weather forecasting. The data assimilation algorithm used at ECMWF formulates the problem mathematically in terms of the minimization of a function that measures how far the model solution is from the actual measurements. This function depends on millions of variables and so it must be minimized approximately using numerical optimization methods. Current methods cannot be easily used in a parallel computing environment and thus there is a need for new parallel approaches.

Recently, the use of randomized algorithms for data assimilation have been investigated. While highly parallelizable, these methods are not always computationally efficient.  This project will develop new randomized approaches that are efficient and can be run on large-scale parallel computers.   New theory will be developed while experiments will include the use of an idealized assimilation system on which different ideas can be tested, followed by the application of the most promising methods to the ECMWF numerical weather prediction system.

The student will have the opportunity to spend time working at ECMWF and to attend ECMWF training courses on data assimilation as well as advanced training courses at the University of Reading organized by the Data Assimilation Research Centre.

Eligibility: 

• Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree or equivalent in mathematics or a subject with a strong mathematical content, including knowledge of numerical methods. Experience of programming is desirable and an interest in developing numerical algorithms and implementing them in parallel to solve real-world problems is essential.
• Due to restrictions on the funding, applicants must be citizens of an ECMWF member state. https://www.ecmwf.int/en/about/who-we-are/member-states

Funding Details:  

• Starts 18 September 2023.
• 3-year award plus possible 6-month extension.
• Funding covers full tuition fees plus UKRI stipend.

How to apply:  

To apply click “Apply for a Programme” and create your account and use the link sent by email to start the application process. During the application process please select the PhD in Mathematics

*Important notes*

1. Please quote the reference ‘GS23-001’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
2. When you are prompted by the online application system to upload a research proposal, please omit this step as the project is already defined.

Application Deadline:  20^th January 2023 (interviews to take place in February 2023)

Further Enquiries:  Please contact jennifer.scott@reading.ac.uk

Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria. 

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