Scientist / Senior Scientist to work on dynamical core aspects related to the 4D-VAR Data Assimilation System

1. Position information
Vacancy No
.: VN18-06 Department: Research Grade: A2/A3 Section: Earth System Modelling Job Ref. No.: STF-C/18-06 Reports to: Team Leader Numerical Methods Publication Date: 19 January 2018 Closing Date: 28 February 2018

2. About ECMWF
ECMWF is both a research institute and a 24/7 operational service, producing and disseminating numerical weather predictions to its Member States. ECMWF carries out scientific and technical research directed to the improvement of its forecasts, collects and processes large amounts of observations, and manages a long-term archive of meteorological data. Satellite and in situ observations provide the information for up-to-date global analyses and climate reanalyses of the atmosphere, ocean and land surface.

For details, see www.ecmwf.int/.

3. Summary of the role
An accurate forecast model and accurate initial conditions are essential for producing medium range forecasts. The forecast model together with the 4D-VAR data assimilation system are the two key and intimately interconnected components of the ECMWF Integrated Forecast System (IFS). The role of the 4D-VAR system is to compute the initial state of the atmosphere by minimizing a spacetime depended objective cost function measuring the distance of the initial model state from observations. Solving this computationally expensive problem, requires repeated forward-backward integrations of the tangent-linear (TL) and the adjoint (AD) version of the nonlinear model. Accurate, robust and efficient TL/AD models are essential for producing high quality analyses within the tight time constraints the operational model must satisfy.

The numerical algorithms of the spectral transform, semi-implicit, semi-Lagrangian IFS dynamical core are continuously improved to increase its predictive skill, efficiency and scalability as resolution increases and High Performance Computing (HPC) architectures shift towards exascale designs. It is essential that TL/AD models remain competitive and compatible with the changes of the non-linear forecast model. This is a task that requires significant effort and an ability to often compromise competing development needs in terms of accuracy and stability between the nonlinear and tangentlinear model.

This position sits in the Numerical Methods Team of the Earth System Modelling (ESM) Section of the Research Department. The successful candidate will contribute to TL/AD model developments linked with both the current and future dynamical core options. He or she will be the first point of contact for troubleshooting TL/AD issues collaborating with colleagues in the Physical Processes Team of the Earth System Modelling Section. He or she will be expected to participate in the research and development activities of the Numerical Methods Team related to the maintenance and further improvement of the semi-implicit, semi-Lagrangian formulation of the hydrostatic and nonhydrostatic dynamical core options of the IFS.

An important activity in the Numerical Methods Team is the development of an alternative nonhydrostatic, Finite-Volume dynamical core and other work assigned to this role will be to develop a TL/AD counterpart for the new dynamical core option.

Ideally, the successful candidate will have very good knowledge of numerical methods for global atmospheric models as well as strong expertise in tangent-linear and adjoint methods used in variational data assimilation or parameter estimation. Familiarity and experience in semi-implicit, semi-Lagrangian modelling will be an advantage.

This position involves significant interaction with all the other teams within the Earth System Modelling Section as well as across other sections. Therefore, the successful candidate must have the ability and willingness to communicate effectively with other scientists as well the ability to organise and plan the scope of the work on TL/AD developments appropriately.

4. Main duties and key responsibilities

  • Developing TL/AD models for the current and future IFS dynamical core and maintaining/testing these models between IFS software cycles
  • Developing/maintaining diagnostic tools for validating TL/AD developments
  • Developing a TL/AD model framework for adjoint sensitivities and for use in parameter optimization
  • Acting as the first point of contact in the Earth System Modelling Section for operational issues related with TL/AD models
  • Participate in numerical algorithm developments for the IFS dynamical core such as improvement of the semi-Lagrangian, semi-implicit hydrostatic and non-hydrostatic formulations

5. Personal attributes

  • Excellent interpersonal and communication skills
  • Excellent analytical and problem-solving skills and ability to interpret complex results
  • Self-motivated, and able to work with minimal supervision
  • Dedication and enthusiasm to work in a team
  • Ability to work efficiently and complete diverse tasks in a timely manner

6. Qualifications and experience required

Education A university degree in Meteorology Physics, Mathematics or a related subject.
A PhD in a related subject with strong numerical modelling content is desirable but not essential.

Experience Experience with tangent-linear and adjoint models and their use in variational data assimilation is essential.
Experience with numerical modelling methods for global NWP and complex geophysical models is essential.
Excellent coding skills, ideally both in C++ and Fortran and experience with large scientific codes in an HPC environment is essential.
Experience with semi-implicit, semi-Lagrangian atmospheric models is highly desirable.

Knowledge and skills (including language)
Good knowledge in meteorology is essential.
Ability to read and organise complex code structures that combine different algorithmic concepts.
Familiarity with Linux and scripting languages such as python, IDL or similar and knowledge of source code management systems is desirable.
Candidates must be able to work effectively in English and interviews will be conducted in English.
A good knowledge of one of the Centre’s other working languages (French or German) would be an advantage.

7. Other information

Grade remuneration

This position is graded A2/A3 in line with the scales of the Co‑ordinated Organisations.

For the A2 grade, the minimum annual basic salary will be £58,238.40 net of tax.

For the A3 grade, the minimum annual basic salary will be £71,861.52 net of tax.

Recruitment at the higher grade may be considered where the level of the candidate’s skills and experience exceeds the requirements of the position. This will be at the discretion of the organisation. This position is assigned to the employment category STF-C as defined in the Staff Regulations.

Full details of salary scales and allowances are available on the ECMWF website at www.ecmwf.int/en/about/jobs, including the Centre’s Staff Regulations regarding the terms and conditions of employment.

Starting date: As soon as possible.
Length of contract: Four years, with the possibility of a further contract.
Location: The position will be based in the Reading area, in Berkshire, United Kingdom.

8. How to apply
Please apply by completing the online application form available at www.ecmwf.int/en/about/jobs.

ECMWF has an Equal Opportunities Policy and applications from all suitably qualified candidates are welcome.

Staff are usually recruited from among nationals of the following Member States and Co-operating States:

Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, former Yugoslav Republic of Macedonia, France, Hungary, Germany, Greece, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and the United Kingdom.

Staff from other countries may be considered in exceptional cases.


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European Centre for Medium-Range Weather Forecasts - ECMWF

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2018-02-28


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Företag

European Centre for Medium-Range Weather Forecasts - ECMWF

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Sista ansökningsdag

2018-02-28


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Maila annonsen till mig