JOB SUMMARY

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.

EDUCATION

• 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

• 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.

• 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