Articles | Volume 21, issue 17
Atmos. Chem. Phys., 21, 13247–13267, 2021
https://doi.org/10.5194/acp-21-13247-2021
Atmos. Chem. Phys., 21, 13247–13267, 2021
https://doi.org/10.5194/acp-21-13247-2021

Research article 07 Sep 2021

Research article | 07 Sep 2021

Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event

Joffrey Dumont Le Brazidec et al.

Related authors

A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
Colin Grudzien and Marc Bocquet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-306,https://doi.org/10.5194/gmd-2021-306, 2021
Preprint under review for GMD
Short summary
On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020,https://doi.org/10.5194/gmd-13-1903-2020, 2020
Short summary
Diagnosing spatial error structures in CO2 mole fractions and XCO2 column mole fractions from atmospheric transport
Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez
Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019,https://doi.org/10.5194/acp-19-12007-2019, 2019
Short summary
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019,https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136,https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Surface deposition of marine fog and its treatment in the Weather Research and Forecasting (WRF) model
Peter A. Taylor, Zheqi Chen, Li Cheng, Soudeh Afsharian, Wensong Weng, George A. Isaac, Terry W. Bullock, and Yongsheng Chen
Atmos. Chem. Phys., 21, 14687–14702, https://doi.org/10.5194/acp-21-14687-2021,https://doi.org/10.5194/acp-21-14687-2021, 2021
Short summary
Assessing the potential efficacy of marine cloud brightening for cooling Earth using a simple heuristic model
Robert Wood
Atmos. Chem. Phys., 21, 14507–14533, https://doi.org/10.5194/acp-21-14507-2021,https://doi.org/10.5194/acp-21-14507-2021, 2021
Short summary
Aerosol effects on electrification and lightning discharges in a multicell thunderstorm simulated by the WRF-ELEC model
Mengyu Sun, Dongxia Liu, Xiushu Qie, Edward R. Mansell, Yoav Yair, Alexandre O. Fierro, Shanfeng Yuan, Zhixiong Chen, and Dongfang Wang
Atmos. Chem. Phys., 21, 14141–14158, https://doi.org/10.5194/acp-21-14141-2021,https://doi.org/10.5194/acp-21-14141-2021, 2021
Short summary
The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model
Huisheng Bian, Eunjee Lee, Randal D. Koster, Donifan Barahona, Mian Chin, Peter R. Colarco, Anton Darmenov, Sarith Mahanama, Michael Manyin, Peter Norris, John Shilling, Hongbin Yu, and Fanwei Zeng
Atmos. Chem. Phys., 21, 14177–14197, https://doi.org/10.5194/acp-21-14177-2021,https://doi.org/10.5194/acp-21-14177-2021, 2021
Short summary
“Warm cover”: precursory strong signals for haze pollution hidden in the middle troposphere
Xiangde Xu, Wenyue Cai, Tianliang Zhao, Xinfa Qiu, Wenhui Zhu, Chan Sun, Peng Yan, Chunzhu Wang, and Fei Ge
Atmos. Chem. Phys., 21, 14131–14139, https://doi.org/10.5194/acp-21-14131-2021,https://doi.org/10.5194/acp-21-14131-2021, 2021
Short summary

Cited articles

Abida, R. and Bocquet, M.: Targeting of observations for accidental atmospheric release monitoring, Atmos. Environ., 43, 6312–6327, https://doi.org/10.1016/j.atmosenv.2009.09.029, 2009. a
Anderson, J. L.: A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, J. Climate, 9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2, 1996. a
Atchadé, Y. F., Roberts, G. O., and Rosenthal, J. S.: Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo, Stat. Comput., 21, 555–568, https://doi.org/10.1007/s11222-010-9192-1, 2011. a
Baklanov, A. and Sørensen, J. H.: Parameterisation of radionuclide deposition in atmospheric long-range transport modelling, Phys. Chem. Earth Pt. B, 26, 787–799, https://doi.org/10.1016/S1464-1909(01)00087-9, 2001. a
Baragatti, M.: Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance, Thèse de doctorat, Aix-Marseille 2, 2011. a
Download
Short summary
The assessment of the environmental consequences of a radionuclide release depends on the estimation of its source. This paper aims to develop inverse Bayesian methods which combine transport models with measurements, in order to reconstruct the ensemble of possible sources. Three methods to quantify uncertainties based on the definition of probability distributions and the physical models are proposed and evaluated for the case of 106Ru releases over Europe in 2017.
Altmetrics
Final-revised paper
Preprint