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Scientist on Coupled Satellite Data Assimilation (LSTM)

Bonn | Reading

  • Organization: ECMWF - European Centre for Medium-Range Weather Forecasts
  • Location: Bonn | Reading
  • Grade: Junior level - A2 - Grade band
  • Occupational Groups:
    • Statistics
    • Physics and Mathematics
    • Outer space and satellite technology
    • Information Technology and Computer Science
    • Scientist and Researcher
  • Closing Date: 2024-06-16

Job reference: VN24-52
Salary and Grade: Grade A2 GBP 71,451 (Reading/UK) or EUR 86,824 (Bonn/Germany) NET annual basic salary + other benefits
Deadline for applications: 16/06/2024
Department: Research
Location: Reading, UK or Bonn, Germany
Contract type: STF-PL
Publication date: 14/05/2024
Contract Duration: 3 years

Job Description

The role

We have an exciting opportunity for a highly motivated scientist to advance our exploitation of satellite data in ECMWF’s coupled global forecasting system. The role will prepare the advanced use of observations from the Land Surface Temperature Mission (LSTM), planned to be launched by the European Space Agency (ESA) in 2028. The Thermal Infrared (TIR) instrument of LSTM will provide high spatio-temporal resolution observations of all land and coastal areas with high radiometric accuracy. The aim will be to use this information in an optimal way to initialise land surface temperature in our global Numerical Weather Prediction (NWP) forecasts. 

The successful candidate will work at the forefront of developing our capabilities to use infrared observations to analyse land surface temperature variables in a coupled land-atmosphere data assimilation system, using a combination of machine learning and physical methods. Initially, observations from existing instruments with similar characteristics will be employed to develop ways to assimilate the surface temperature information from LSTM.  The candidate will also develop the dataflow for LSTM observations in the ECMWF systems.  

The role will be based in a team dedicated to advancing the exploitation of surface observations in land and coupled data assimilation, and the research will feed directly into ECMWF’s operational global NWP system. The position is funded by ESA as part of the Data Assimilation and Numerical Testing for Copernicus EXpansion missions (DANTEX) initiative.  DANTEX is a three-year project funded by the European Space Agency to establish scientific and technical capability for Copernicus missions, notably the Copernicus Expansion Missions CIMR, CRISTAL and LSTM, and also the existing Sentinel-1 mission, which is currently under exploited in Numerical Weather Prediction. This phase of DANTEX will run from late in 2024 to late in 2027 and future phases are foreseen (subject to funding).   

The team

The successful candidate will join the Coupled Assimilation Team, dedicated to coupled data assimilation methodology developments as well as land and ocean data assimilation developments. One of the key focus of the team is to enhance the exploitation of satellite observations sensitive to the surface. The team has pioneered the developments of coupled data assimilation methods and implementation towards an Earth system coupled data assimilation for operational applications. The team is part of the Earth System Assimilation Section in the Research Department of ECMWF. The Section is responsible for developing and optimising ECMWF’s assimilation system, one of the most advanced systems for the exploitation of satellite data for operational NWP and reanalyses. There will be strong collaboration with other teams across ECMWF, particularly the Infrared Observations Team.

About ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world-leader in weather and environmental forecasting. As an international organisation we serve our members and the wider community with global weather predictions and data that is critical for understanding and solving the climate crisis. We function as a 24/7 research and operational centre with a focus on medium and long-range predictions, holding one of the largest meteorological data archives in the world. The success of our activities builds on the talent of our scientists and experts, strong partnerships with 35 Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies and machine learning across our operations. ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/supercomputer in Bologna, Italy, and a large presence in Bonn, Germany.  

ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring. 

See www.ecmwf.int for more info about what we do.  

Main duties and key responsibilities

  • Develop the ECMWF land data assimilation system to enable the exploitation of land surface temperature from LSTM-like observations
  • Adapt novel ways to represent LSTM-like radiances combining machine learning and physical radiative transfer concepts as required 
  • Perform and analyse land surface and coupled data assimilation experiments to evaluate the benefit of LSTM data, using existing observations as proxy for LSTM data 
  • Ensure timely delivery of relevant results to the European Space Agency 
  • Communicate and document scientific results and software developments in technical reports, journal publications, conferences and meetings as appropriate 

What we are looking for

  • Excellent analytical and problem-solving skills with a proactive and constructive approach 
  • Ability to succeed both independently and as part of multidisciplinary and geographically distributed teams 
  • Flexibility, with the ability to adapt to changing priorities 
  • Excellent interpersonal and communication skills 
  • Ability to work efficiently and complete diverse tasks in a timely manner 

Education:

  • A PhD or equivalent proven research experience in Earth System Science, Physics, Applied Mathematics, Computer Science, or a related discipline

Experience, knowledge and skills:

  • Experience in satellite data analysis, radiative transfer or data assimilation  
  • Some experience with land data assimilation or the use of infrared radiances would be an advantage 
  • Experience with machine learning is highly desirable, ideally for geophysical applications 
  • Experience with performing statistical analyses and preparing scientific figures   
  • Strong programming skills, ideally in Python, Fortran, and UNIX shell scripting or equivalent 
  • Experience with working on high-performance computing platforms in Unix/Linux-based environments would be an advantage 

We encourage you to apply even if you don’t feel you meet precisely all these criteria.

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) is an advantage but not required.

Other information

Grade remuneration

The successful candidates will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. The position is assigned to the employment category STF-PL as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date:             1st October 2024

Length of contract:  3 years

Candidates are expected to relocate to the duty station. As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking, including away from the duty station (within the area of our member states and co-operating states).

Interviews by videoconference (MS Team) are expected to be arranged within a month of the closing date. 

Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.

Who can apply

Applicants are invited to complete the online application form by clicking on the apply button below.

At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.

Applications are invited from nationals from ECMWF Member States and Co-operating States, as well as from all EU Member States.  In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy.   Applications from nationals from other countries may be considered in exceptional cases. 

ECMWF Member States and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom. 

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