Preprints
https://doi.org/10.5194/gmd-2020-162
https://doi.org/10.5194/gmd-2020-162

Submitted as: development and technical paper 23 Sep 2020

Submitted as: development and technical paper | 23 Sep 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

Pieter De Meutter1,2,3, Ian Hoffman1, and Kurt Ungar1 Pieter De Meutter et al.
  • 1Radiation Protection Bureau, Health Canada, 775 Brookfield Road, Ottawa, Canada
  • 2Belgian Nuclear Research Institute, Boeretang 200, Mol, Belgium
  • 3Royal Meteorological Institute of Belgium, Ringlaan 3, Brussels, Belgium

Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident, or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing credible intervals on the inferred source parameters in a natural way. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

Pieter De Meutter et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Pieter De Meutter et al.

Data sets

SRS data Pieter De Meutter and Andy Delcloo https://doi.org/10.5281/zenodo.4003640

Pieter De Meutter et al.

Viewed

Total article views: 325 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
228 92 5 325 10 10
  • HTML: 228
  • PDF: 92
  • XML: 5
  • Total: 325
  • BibTeX: 10
  • EndNote: 10
Views and downloads (calculated since 23 Sep 2020)
Cumulative views and downloads (calculated since 23 Sep 2020)

Viewed (geographical distribution)

Total article views: 292 (including HTML, PDF, and XML) Thereof 291 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jan 2021
Download
Short summary
Inverse atmospheric transport modelling is an important tool in several disciplines. However, the specification of atmospheric transport model error remains challenging. In this paper, we employ a state-of-the-art ensemble technique combined with a state-of-the-art Bayesian inference algorithm to infer point sources. Our research helps to fill the gap in our understanding of model error in the context of inverse atmospheric transport modelling.