Improved deposition modelling based on combining state-of-the-art atmospheric dispersion simulations and numerical weather data.

Promoter

Termonia Piet, (Universiteit Gent (UGent)), piet.termonia@ugent.be

SCK•CEN Mentor

Camps Johan, jcamps@sckcen.be, +32 (0)14 33 27 61

Expert group

Crisis Management and Decision Support

Short project description

Atmospheric transport and dispersion models are necessary tools to assess the impact of radioactive gases and particulates released into the atmosphere. Such releases can occur routinely or accidently during nuclear activities. The released radioactive gases and particulates, except for noble gases, are subject to wet deposition under influence of precipitation. Wet deposition can locally result in high levels of radioactive surface contamination, and therefore an accurate modelling system is important to correctly estimate the contamination and its impact on the population. However, wet deposition and its driver, precipitation, are both difficult to model. During this PhD, several methods will be explored to understand these uncertainties and to improve deposition modelling, taking advantage of of the latest developments in numerical weather prediction modelling and radar data.
The Lagrangian particle model Flexpart (Stohl et al, 2005) will be used, which is an open source code and which is widely used. Flexpart can be used to simulate the transport, dispersion, radioactive decay and deposition of both (noble) gases and particulates. The model has previously been used at SCK•CEN, in collaboration with the Royal Meteorological Institute of Belgium, to assess the origin of elevated radioactive xenon detections after the North Korean nuclear bomb test of 2013 and 2016 (De Meutter et al., 2017 and 2018). The Flexpart model will be coupled to radar data and numerical weather prediction data provided by the Royal Meteorological Institute of Belgium.
New opportunities related to uncertainty quantification of the spatial and temporal representation of precipitation fields in the numerical weather prediction model fields will be explored. The resolution of the numerical weather prediction model is expected to be important and it is therefore necessary to investigate the impact of changing the horizontal resolution of the numerical weather prediction model. This will be done by moving from global numerical weather prediction models (such as ECMWF at 8 km horizontal grid spacing) towards limited-area numerical weather prediction models where local processes are better resolved (such as the ALARO/AROME high resolution ensemble system that consists of 22 ensemble members and has horizontal grid spacing of 2.5 km).
Further, the use of monitoring radioactivity in rainwater will be explored, as a compliment to monitor airborne concentrations, in the context of inverse modelling for source reconstruction and data assimilation in case of a nuclear or radiological event. The results will be applied to the ruthenium-106 measurements that were made in autumn 2017 in Europe. This will serve as a case study to test and refine the developed methodologies.
 
This work is carried out in close collaboration with the Royal Meteorological Institute of Belgium (KMI) and co-promoted by A. Delcloo.
 
Stohl, A., Forster, C., Frank, A., Seibert, P., & Wotawa, G. (2005). The Lagrangian particle dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics, 5(9), 2461-2474.
 
De Meutter, P., Camps, J., Delcloo, A., & Termonia, P. (2017). Assessment of the announced North Korean nuclear test using long-range atmospheric transport and dispersion modelling. Scientific Reports, 7, 8762.
 
De Meutter, P., Camps, J., Delcloo, A., & Termonia, P. (2018). Source localisation and its uncertainty quantification after the third DPRK nuclear test. Scientific Reports, 8, 10155.

The minimum diploma level of the candidate needs to be

Master of sciences , Master of sciences in engineering

The candidate needs to have a background in

Physics , Mathematics , Informatics , Background in meteorology (especially numerical weather data) is an important added value

Estimated duration

4 years
Before applying, please consult the guidelines for application for PhD.