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Numerical Weather Prediction Research Intern

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We are looking for an enthusiastic and curious research intern who is keen to explore and learn more about meteorology and numerical weather prediction. The intern will be part of an active research team that is working on improving the fidelity of weather forecasts using the operational Met Office Unified Model. The internship will focus on analysing data from numerical simulations to understand how numerical methods interact with the parameterized physical processes in atmospheric models. There will be opportunities to collaborate with researchers from Exeter University as well as the UK Met Office. 

 

Please note this role is only open to students from the following disciplines Engineering, Maths, CS, Physics and Nat Sci.


This role is part of the Student Campus Partnership (SCP) scheme and is for 140 hours in total from April to June 2024. This role offers Hybrid working where you will work from Hope Hall, Streatham Campus with partial remote working.

Key Details

Main Supervisor: Dr Georgios Efstathiou

Estimated pay: £13.45 per hour (£12.00 basic hourly rate + £1.45 holiday)

Working Hours: 140 hours in total, no more than 15 hours per week during term time

Application Deadline: 11/03/2024

Interview Date: From W/C 18/03/2024

Start Date: 29/04/2024

End Date: 28/06/2024

 

Purpose of the Role

To analyse data from numerical simulations and contribute towards better understanding of the coupling between numerical methods and physical parameterizations in numerical weather prediction models.

 

Duties and Responsibilities 

You will use Python suites to post-process and analyse datasets from numerical simulations of realistic/idealized case studies. You will also be trained to use numerical models like the Met Office Unified Model and other research atmospheric models. The research work will involve performing simulations and visualizing output from different atmospheric models and configurations. You will be offered support to write a brief scientific report, based on your work during the project and opportunities to present your research in meetings/seminars. You will collaborate with Dr Georgios Efstathiou, Prof Robert Beare and Dr Dimitar Vlaykov (Mathematics and Statistics) as well as researchers from the Met Office.

 

Training and Development - What Skills and Experience will this Opportunity Provide?

You will gain an insight on working on an active research project, acquiring research experience in meteorological modelling and numerical weather prediction. You will also develop skills on numerical simulations and methods, scientific programming as well as scientific writing and presentation. These skills are essential for PhD studies, research or academic careers or working for large scientific organisations such as the UK Met Office. 

 

You will be asked to complete the university’s mandatory online training in: 

  • Prevent Duty - Inclusive, Cohesive and Safe Universities 
  • Equality and Diversity Introduction 
  • Health and Safety Introduction 
  • Information Governance

 

Support Available

  • You will be offered training on numerical weather prediction models and the tools/methods used for post-processing and analysing model output.
  • There will be regular meetings with the academic supervisor, members of the research team and the Met Office. An experienced post-doctoral researcher (Dr Dimitar Vlaykov) will be assigned as workplace mentor to the intern.
  • Students are offered 1:1 support from the Internships Team.

 

The Benefits

 

About You 

Degree and Attainment

  • Current student at the University of Exeter
  • Please note these roles are open to students from the following disciplines Engineering, Maths, CS, Physics and Nat Sci.

 

Skills

  • Basic programming skills in Python/Matlab with some background in applied mathematics (mathematical modelling/numerical methods) are essential for this placement

 

Knowledge

  • Strong interest in numerical modelling, weather science or geophysical fluid dynamics in general
  • Background in atmospheric science is desirable.