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July 2013
Message 87

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[Met-jobs] Met Office vacancy: Land Surface Data Assimilation Scientist

From "Roger Brugge" <r.brugge@reading.ac.uk>
To "met-jobs@lists.reading.ac.uk" <met-jobs@lists.reading.ac.uk>
Date Tue, 23 Jul 2013 14:09:50 +0000

The following vacancy has been announced at:
http://www.metoffice.gov.uk/about-us/jobs/current-vacancies/002815

Land Surface Data Assimilation Scientist
Salary: Starting £25,500 and for exceptional candidates up to £29,100 or 
£29,930 and for exceptional candidates up to £35,040 + competitive benefits, 
including Civil Service Pension

Generic role: Scientist or Senior Scientist
Profession: Science and Engineering
Permanent. Full time at Met Office, Exeter
Opening date for applications: 24 July 2013
Closing date for applications: 30 August 2013
Background information
The Data Assimilation and Ensembles (DAE) group consists of six research teams, 
totalling about 40 scientists, who are carrying out research and development in 
observations processing, data assimilation and ensemble forecasting. The work 
of DAE is focussed on improving the quality of Met Office forecasts.
The Earth System Data Assimilation team currently focuses on two areas: data 
assimilation development for the land surface, and regional reanalysis. The 
team has responsibility for developing the SURF system, providing Numerical 
Weather Predicition (NWP) models with analyses of land surface variables: soil 
moisture and soil temperature. These give a lower boundary condition for the 
atmospheric models.
Recently an Extended Kalman Filter (EKF) has been developed that combines 
satellite observations with screen-level data to analyse values for our global 
model. There are challenges in adapting this to high resolution over the UK. 
These include adapting the EKF to use observed precipitation, as featured in 
the hydrological model within the UKPP nowcasting system. There is also scope 
to develop the EKF into an Ensemble Kalman Filter, to meet the needs of 
ensemble NWP. Increasingly there is a requirement for reanalysis of past data. 
Historical datasets of land surface observations from satellite are available 
but their use would need to be assessed.
The postholder would work as part of a small group improving land surface DA in 
one of the areas above. They would broaden their knowledge through research and 
through interaction with others both in high-resolution modelling and in the 
land surface community. They can expect to take the lead in research and 
development leading to improvements for high-resolution NWP. The postholder 
will be able to present their work in international conferences and in the 
peer-reviewed literature. They would be encouraged to build relationships with 
other institutions, looking to set up collaborative projects.
Specific job purpose
Develop land surface data assimilation to meet the new demands required of it.
Specific job responsibilities
·         To develop expertise in land surface data assimilation.
·         To liaise with others in the Met Office working on land surface 
modelling and data assimilation.
·         To understand and work towards meeting the requirements of UK 
atmospheric models, ensembles or reanalyses for land surface data assimilation.
·         To contribute to the presentation and publication/documentation of 
work internally and externally in order to communicate advice, maintain 
scientific/technical capability and to promote Met Office reputation.
·         To build on existing collaborations and consider new partnerships.
Qualifications, skills and abilities required
Essential
·         A degree (2:1 or above) in a physical science, mathematics or other 
related discipline or equivalent experience.
·         Scientist: Competence in postgraduate research and development in 
data assimilation, or a related field, as demonstrated by award of a PhD, or 
equivalent experience.
Senior Scientist: PhD in relevant subject and/or extensive experience and 
proven track record of scientific research.
·         The ability to find novel solutions to scientific problems.
·         Ability to write effective scientific software and to work with large 
computer codes.
·         Ability to effectively communicate their research both in person and 
in writing.
·         Scientist: Ability to work effectively both as an independent 
scientist but also as part of a larger team involving colleagues at the Met 
Office and international UM partners.
Senior Scientist: Evidence of ability to provide scientific/technical 
leadership or mentoring to junior staff.
Desirable
·         Knowledge of data assimilation techniques.
·         Knowledge/experience in land surface modelling or observation 
research and development.
·         Experience of developing suites and running trials within a Unified 
Model environment.







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