Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1237-2021
https://doi.org/10.5194/gmd-14-1237-2021
Development and technical paper
 | 
08 Mar 2021
Development and technical paper |  | 08 Mar 2021

On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

Pieter De Meutter, Ian Hoffman, and Kurt Ungar

Related authors

An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023,https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary

Related subject area

Atmospheric sciences
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023,https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023,https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023,https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023,https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023,https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary

Cited articles

Becker, A., Wotawa, G., De Geer, L.-E., Seibert, P., Draxler, R. R., Sloan, C., D'Amours, R., Hort, M., Glaab, H., Heinrich, P., Grillon Vyacheslav Shershakov, Y., Katayama, K., Zhang, Y., Stewart, P., Hirtl, M., Jean, M., and Chen, P.: Global backtracking of anthropogenic radionuclides by means of a receptor oriented ensemble dispersion modelling system in support of Nuclear-Test-Ban Treaty verification, Atmos. Environ., 41, 4520–4534, https://doi.org/10.1016/j.atmosenv.2006.12.048, 2007. a
Bocquet, M.: High-resolution reconstruction of a tracer dispersion event: application to ETEX, Q. J. Roy. Meteor. Soc., 133, 1013–1026, https://doi.org/10.1002/qj.64, 2007. a
Bonavita, M., Hólm, E., Isaksen, L., and Fisher, M.: The evolution of the ECMWF hybrid data assimilation system, Q. J. Roy. Meteor. Soc., 142, 287–303, https://doi.org/10.1002/qj.2652, 2016. a
Bossew, P., Gering, F., Petermann, E., Hamburger, T., Katzlberger, C., Hernandez-Ceballos, M., De Cort, M., Gorzkiewicz, K., Kierepko, R., and Mietelski, J.: An episode of Ru-106 in air over Europe, September–October 2017–Geographical distribution of inhalation dose over Europe, J. Environ. Radioactiv., 205, 79–92, https://doi.org/10.1016/j.jenvrad.2019.05.004, 2019. a, b
Currie, L. A.: Limits for qualitative detection and quantitative determination. Application to radiochemistry, Analyt. Chem., 40, 586–593, https://doi.org/10.1021/ac60259a007, 1968. a, b
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.