Job Description

Job summary

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We have an exciting opportunity for a highly motivated scientist to advance our exploitation of satellite data in ECMWF's land data assimilation system. The role will develop the use of observations from GNSS-R (Global Navigation Satellite Systems Reflectometry) in preparation of the European Space Agency (ESA) HydroGNSS mission that will be launched in late 2025. HydroGNSS will focus on land applications and targets four hydrological variables related to Essential Climate Variables or ECVs (soil moisture, wetlands/inundation, freeze-thaw state and forest biomass). The aim of the role will be to use GNSS-R information in an optimal way to initialise soil moisture in our global land data assimilation system and to assess the impact on Numerical Weather Prediction (NWP) and potential for future climate reanalysis.


The successful candidate will work at the forefront of developing our capabilities to use GNSS reflectometry observations to analyse land surface variables in a land 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 GNSS-R information. The candidate will also develop the dataflow for GNSS-R observations in the ECMWF land data assimilation system.


The role will be based in a team dedicated to advancing the exploitation of satellite observations to constrain Earth surfaces. The position is funded by ESA as part of the GNSS-R land data assimilation (DA) study.

The team

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The successful candidate will join the Coupled Assimilation Team, dedicated to land and ocean data assimilation developments as well as coupled data assimilation methodology developments. A 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 the Research Department of ECMWF.

Your responsibilities

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Investigate the exploitation of land surface information from GNSS-R reflectometry data with a focus on soil moisture Develop and implement machine learning-based observation operators to represent GNSS-R data in the ECMWF land data assimilation system Perform and analyse land data assimilation and coupled NWP experiments to evaluate the benefit of GNSS-R data, using existing GNSS-R 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're looking for

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Excellent analytical and problem-solving skills with a proactive and constructive approach Flexibility, with the ability to adapt to changing priorities Ability to work autonomously and as part of multidisciplinary and geographically distributed teams Excellent interpersonal and communication skills Highly organised with the capacity to work on a diverse range of tasks to tight deadlines

Education

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The candidate should have a PhD or equivalent proven research experience in Earth System Science, Physics, Applied Mathematics, Computer Science, or a related discipline

Experience, Knowledge and Skills

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Experience in satellite data analysis, radiative transfer or data assimilation Some experience with land data assimilation or the use of GNSS-R data 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 Candidates must be able to work effectively in English. Knowledge of one of ECMWF's other working languages (French or German) would be an advantage
We encourage you to apply even if you don't feel you fully meet all these criteria.

About ECMWF

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The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high-quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.


The success of our activities depends on the funding and partnerships of our 35 Member and Co-operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful supercomputing capabilities, are the core of a 24/7 research and operational centre with a focus on medium and long-range predictions. We also hold one of the largest meteorological data archives in the world.


Our mission: Deliver global numerical weather predictions focusing on the medium-range and monitoring of the Earth system to and with our Member States.


Our vision: World-leading monitoring and predictions of the Earth System enabled by cutting-edge physical, computational and data science, resulting from a close collaboration between ECMWF and the members of the European Meteorological Infrastructure, will contribute to a safe and thriving society.


In addition, ECMWF has established 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, as well as being a contributor to the Copernicus Emergency Management Service. 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.


During COP 27 in November 2022, the UN Secretariat General launched the 'Early Warning for All' initiative, with the ambition that anyone in the world is covered by an early warning system or climate service by 2027, so that they are informed on upcoming climate-related disaster and lives can be saved. ECMWF is one of the world-leading centres for global hydro-meteorological forecasting and climate services, operating the Hydrological Forecast Computational Centre of the Copernicus Emergency Management Service since 2011. Every day, we generate hydro-meteorological forecast data for thousands of points and make the information available to registered users through dedicated web applications such as the CEMS European and Global Flood Awareness Systems, or data services such as the Climate Data Store. In a process of continuous evolution and improvement, ECMWF also conducts research on how to improve its Early Warning Systems and Climate Services especially on hydrological-related hazards such as floods and droughts. This modelling position is at the core of the next-generation of Early Warning Systems needed to inform and improve emergency response related to hydrology in any part of the Earth system.


ECMWF is a multi-site organisation, with its headquarters in Reading, UK, a data centre in Bologna, Italy, and a large presence in Bonn, Germany as a central location for our EU-related activities. ECMWF is internationally recognised as the voice of expertise in numerical weather predictions for forecasts and climate science.


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

About the GNSS-R Land Data Assimilation Project

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The GNSS-R land DA study is a two-year project funded by the European Space Agency to establish scientific and technical capability to explore the potential of GNSS-R data assimilation over land surfaces for NWP and climate reanalysis applications. The project will provide 20 months of funding and it will run from July 2025 to June 2027.

Other information

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Grade remuneration:

The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. In addition to basic salary, ECMWF also offers an attractive benefits package. To find out more about working with us and for full details of salary scales and allowances, please visit www.ecmwf.int/en/about/jobs/working-ecmwf.

Starting date:

01 July 2025

Location:

Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)

Remote work:

As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking. We allow for remote work 10 days/month away from the office, including up to 80 days/year away from the duty station country (within the area of our member states and co-operating states).


Interviews by videoconference (MS Teams) are expected to take place within a month after the closing date. If you require any special accommodations in order to participate fully in our recruitment process, please let us know.


To contact the ECMWF Recruitment Team, please email jobs@ecmwf.int.

Who can apply

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Applicants are invited to complete the online application form by clicking on theapplybutton 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.


ECMWF Member 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, Turkiye and the United Kingdom.

In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy (note: ECMWF will not be able to assist with leaving the country).

Applications from nationals from other countries may be considered in exceptional cases.