Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1237–1252, 2021
https://doi.org/10.5194/gmd-14-1237-2021
Geosci. Model Dev., 14, 1237–1252, 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 et al.

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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
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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.