|From||"Roger Brugge" <firstname.lastname@example.org>|
|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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