Research position (post-doc): Assimilation of Dual-Polarization Radar Observations in Numerical Weather Prediction Model
KU Leuven
The Department of Civil Engineering at KU Leuven, Belgium, is seeking a researcher to join our interdisciplinary Unit of Hydraulics and Geotechnics (*). Our unit conducts robust research on water cycle, river systems, urban drainage systems, and the impacts of climate change. Recently, we have expanded our research focus to include the observation and forecasting of high-impact weather events.
This position is a part of the EXPRIMA project titled “Probabilistic Nowcasting and Forecasting of Extreme Precipitation for Impact-Based Warning Systems”. This project is a collaborative effort between KU Leuven and the Royal Meteorological Institute of Belgium (RMIB) under the FED-tWIN platform (++). The FED-tWIN platform facilitates research collaboration and excellence between Belgian universities and federal scientific institutions, coordinated by the Belgian Science Policy (BELSPO). The researcher will be supervised by Prof. Ricardo Reinoso-Rondinel (KU Leuven and RMIB’s Observation Department +++) and co-supervised by Idir Dehmous and Alex Deckmyn (RMIB’s Modeling ALADIN Group ****).
Key words: Extreme precipitation, NWP, ensemble forecasting, data assimilation, radar meteorology.
(*) https://bwk.kuleuven.be/hydr
(++) https://www.belspo.be/belspo/FED-tWIN/index_en.stm
(+++) https://radli.meteo.be/
(****) https://www.meteo.be/en/research-rmi/scope-of-research/numerical-model-alaro/numerieke-weersvoorspellingen
Homepage: https://bwk.kuleuven.be/hydr
Hydrological Sciences (HS)
Natural Hazards (NH)
Extreme weather events, such as heavy precipitation and strong winds, pose significant natural hazards globally, impacting economies and populations. These events are characterized by their spatial and temporal variability and forecast uncertainties. The EXPRIMA project aims to develop innovative observation-driven approaches for forecasting high-impact weather and integrating these forecasts into a nationwide early warning system in Belgium. In this job opening, the researcher will focus on the data assimilation of weather radars operated by the RMIB for forecasting extreme precipitation.
The observation-driven seamless prediction system at RMIB utilizes the ACCORD high-resolution NWP models ALARO and AROME, two convective-permitting configuration models at 1.3 km resolution. These models run operationally in a mini-lagged ensemble prediction system with 6 h forecast production cycle and up to 48 h lead-time. Initial conditions are obtained from the surface analysis of observations from a network of synoptic stations. Recently, a 3-hour 3D-Var assimilation cycle has been implemented within AROME, incorporating more data sources: aircraft AMDAR, radiosonde measurements, and the zenith total delay (ZTD) from GPS stations
Responsibilities:
The primary scientific task is the implementation of a 3D-Var framework to assimilate radar data: reflectivity, vertically integrated liquid, differential reflectivity, and Doppler velocity. The following related tasks are expected:
Pre-process and perform quality control of radar data to account for observational errors.
Establish a structured and homogeneous database.
Optimize radar data coverage by generating super-observations (NWP matching).
Validate the 6-h forecasted precipitation fields with DA using nationwide QPE fields.
(Optional) Explore satellite data assimilation (FCI, ISAI, AMSU-A/B).
Document scientific results in a peer-reviewed journal and present at conferences.
Profile:
We invite candidates who currently hold or are on the verge of completing a PhD in Meteorology, Geosciences, Atmospheric Science, Civil Engineering, Physics, or a related field, and demonstrate a keen interest in high-impact weather. Experienced candidates with an MSc degree are also welcome. The following qualifications are essential for this role:
Academic Performance: Demonstrated excellence or distinction in your Master’s program.
Programming Skills: Proficiency in programming/scripting languages such as Bash, Linux, Python, and R. Skills in Fortran and SQL are also suitable.
Experience: Familiarity with numerical weather prediction models, radars, and remote sensing.
Technical Proficiency: Strong background in mathematics, statistical analysis, and AI tools.
Language Proficiency: Strong command of spoken and written English.
Critical Thinking: Possess critical thinking skills and the ability to work well within a team.
Eligibility: Non-Belgium citizenship and non-Flemish MSc degree due to scholarship mandate.
We are offering a highly competitive full-time Postdoc scholarship for a duration of 1.5 years, with the possibility of an extension of at least 1 year. The selected applicant without a PhD may be admitted as PhD student at KU Leuven.
Interested?
If you have a passion for high-impact weather, precipitation observation and forecasting, flood prediction, and/or modeling methodologies, and believe that you possess the qualifications we are seeking, we encourage you to apply for this position.
To apply, please prepare and submit the following documents: a cover letter (maximum 2 pages), a CV (maximum 2 pages), copies of your BSc, MSc, and PhD (if applicable) transcripts and diplomas, and proof of English proficiency. Please note that the selected candidate must hold an MSc degree before starting the appointment.
For full consideration, please submit your application no later than April 17th, 2025, using our online application tool.
http://www.kuleuven.be/eapplyingforjobs/light/60454240