|From||Frank Kwasniok <F.Kwasniok@exeter.ac.uk>|
|Date||Thu, 13 Jun 2013 23:12:40 +0100|
NERC CASE PhD Studentship at Exeter Climate Systems in collaboration with the Met Office Project: Efficient data assimilation to correct non-Gaussian forecast errors in numerical weather prediction Supervisors: Dr Frank Kwasniok (University of Exeter) and Dr Gordon Inverarity (Met Office, Exeter) Duration: Three and a half years Exeter Climate Systems (http://emps.exeter.ac.uk/mathematics/research/xcs/) is a growing centre of excellence in the application of mathematics and statistics in weather and climate science. It is located within the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. The centre has close working affiliations with the Met Office and the Hadley Centre. We are inviting applications for a PhD studentship in the area of dataassimilation to start in September 2013. The award covers all tuition fees (UK/EU) and a maintenance stipend at research council rate (currently £13,726 per year). As part of the CASE award the student receives an additional £1,200 per year from the Met Office, plus £1,400 on completion of the thesis. A travel and subsistence allowance is also included.
This award is available for UK students. EU candidates must have been resident in the UK for the three years leading up to the PhD to be eligible. International candidates are not eligible. Project description: --- 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. --- For informal enquiries contact Dr Frank Kwasniok at F.Kwasniok@exeter.ac.uk Application criteria: Applicants should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in a relevant subject such as mathematics, statistics, physics or meteorology. Candidates should have a keen interest in the application of mathematics and statistics in weather and climate science, and preferably some experience of programming in Matlab and/or Fortran. How to apply: To apply, go to http://www.exeter.ac.uk/studying/funding/award/index.php?id=1203 and complete the online web form. You should choose "Mathematics PhD Studentship in weather and climate science" from the drop-down menu. You will be asked to submit some personal details and upload a full CV, covering letter and details of two academic referees. Your covering letter should outline your academic interests, prior research experience and reasons for wishing to undertake this project. For general enquiries please contact Liz Roberts at email@example.com The application deadline is Monday 1 July 2013. -- Dr Frank Kwasniok Senior Lecturer in Applied Mathematics College of Engineering, Mathematics and Physical Sciences University of Exeter Harrison Building North Park Road Exeter EX4 4QF United Kingdom E-mail: F.Kwasniok@exeter.ac.uk Tel.: +44 (0)1392 72-3978 Fax: +44 (0)1392 217965 Web: http://empslocal.ex.ac.uk/people/staff/fk206/
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