JOB DESCRIPTION

Job summary

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 use of radiance products from the Copernicus Imaging Microwave Radiometer (CIMR), planned to be launched by the European Space Agency (ESA) in 2029. CIMR will provide a wealth of Earth observation information both on surface conditions as well as the atmosphere. The successful candidate will play a crucial role in preparing the exploitation of this information for global Numerical Weather Prediction (NWP), with a particular focus on sea-surface temperature (SST) information.

A critical aspect of using CIMR data successfully will be to identify and exclude observations affected by Radio Frequency Interference (RFI), that is, human-made signals from radio-transmitters that otherwise over-power the natural geophysical signal. Comparing satellite observations against equivalents calculated from a state-of-the-art NWP system offers a powerful framework for identifying observations affected by such non-natural signals. The successful candidate will apply machine learning concepts to develop new ways of identifying RFI-affected anomalous observations, in the context of our coupled ocean-atmosphere data assimilation system.

The candidate will also investigate ways to enhance the exploitation of SST information from relevant passive microwave instruments in this system. The developments will be done using existing sensors such as the Advanced Microwave Scanning Radiometer (AMSR) 2 or the Soil Moisture Active-Passive (SMAP) instrument.

This role is based in the Microwave Radiances team that is dedicated to advancing the exploitation of satellite microwave radiances, 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.

The team

The Microwave Radiances Team is dedicated to the advanced exploitation of satellite observations for NWP. A key focus of the team is to develop innovative ways to exploit surface-related information alongside atmospheric information in a coupled data assimilation context. The team is part of the Earth System Assimilation Section in the Research Department of ECMWF. The Section is in charge of 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 Coupled Assimilation Team as well as the Data Assimilation Methodology Team.

Your responsibilities

Investigate the exploitation of ocean information from CIMR-like radiances in ECMWF’s coupled assimilation system, with a focus on sea-surface temperature
Develop novel ways to identify RFI-affected observations for CIMR-like radiances in the context of ECMWF’s coupled assimilation system
Evaluate the performance of the RFI detection against existing methods, using existing observations such as AMSR2 as proxy for CIMR 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

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

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 or data assimilation
Experience with machine learning is desirable, ideally for geophysical applications and/or anomaly detection
Some experience with coupled data assimilation, SST estimation or the use of microwave imager radiances would be an advantage
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.

Level of Education: Bachelor Degree

Work Hours: 8

Experience in Months: No requirements