Mathematics PhD Studentship in Weather and Climate Science Efficient data assimilation to correct non-Gaussian forecast errors in numerical weather prediction

Data assimilation allows for the systematic combination of observational data and dynamical models and is a crucial component of numerical weather prediction. A key assumption of standard variational data assimilation techniques is that the forecast errors to be corrected are Gaussian. However, this assumption becomes increasingly invalid as the time between assimilation cycles increases and as the forecast model becomes more nonlinear at higher resolutions. An example of a quantity with non-Gaussian characteristics is visibility, which is an important element of fog and air quality forecasting.

In summary, this project aims to investigate how non-Gaussian forecast errors can be better handled in variational data assimilation. More specifically, it aims to improve the assimilation of visibility data in numerical weather prediction and help implement better techniques in the Met Office’s operational data assimilation system which improve the efficiency and accuracy of the computationally expensive assimilation and forecasting process.

The project will make use of ideas and techniques from statistics, dynamical systems and numerics.

The student will profit from work placements at the Met Office working with their operational data assimilation system.

Application criteria
Applicants should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in mathematics, statistics, physics or meteorology. Candidates should have a keen interest in the application of mathematics and statistics in weather and climate science.

Application deadline: 1st July 2013
Number of awards: 1
Value: Three-and-a-half Year Studentship: Tuition fees (UK/EU) and an annual maintenance allowance at current research council rate.
Duration of award: per year
Contact: Liz Roberts emps-pgr@exeter.ac.uk

How to apply
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