Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Feb 2023
Development and technical paper |
| 09 Feb 2023
| 09 Feb 2023
Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
Joffrey Dumont Le Brazidec
CORRESPONDING AUTHOR
IRSN, PSE-SANTE, SESUC, BMCA, Fontenay-aux-Roses, France
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Marc Bocquet
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Olivier Saunier
IRSN, PSE-SANTE, SESUC, BMCA, Fontenay-aux-Roses, France
Yelva Roustan
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Related authors
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
Nonlin. Processes Geophys., 31, 335–357, https://doi.org/10.5194/npg-31-335-2024, https://doi.org/10.5194/npg-31-335-2024, 2024
Short summary
Short summary
A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023, https://doi.org/10.5194/amt-16-1745-2023, 2023
Short summary
Short summary
Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, https://doi.org/10.5194/acp-21-13247-2021, 2021
Short summary
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.
Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn, Rossella Arcucci, Marc Bocquet, and Sibo Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-2836, https://doi.org/10.5194/egusphere-2025-2836, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We introduce the first denoising diffusion model for wildfire spread prediction, a new kind of generative AI model that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. This allows us to capture the inherent uncertainty of wildfire dynamics. Our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-4028, https://doi.org/10.5194/egusphere-2024-4028, 2025
Short summary
Short summary
This paper presents a four-dimensional variational data assimilation system based on a neural network emulator for sea-ice thickness, learned from neXtSIM simulation outputs. Testing with simulated and real observation retrievals, the system improves forecasts and bias error, performing comparably to operational methods, demonstrating the promise of sea-ice data-driven data assimilation systems.
Alexis Squarcioni, Yelva Roustan, Myrto Valari, Youngseob Kim, Karine Sartelet, Lya Lugon, Fabrice Dugay, and Robin Voitot
Atmos. Chem. Phys., 25, 93–117, https://doi.org/10.5194/acp-25-93-2025, https://doi.org/10.5194/acp-25-93-2025, 2025
Short summary
Short summary
This study highlights the interest of using a street-network model to estimate pollutant concentrations of NOx, NO2, and PM2.5 in heterogeneous urban areas, particularly those adjacent to highways, compared with the subgrid-scale approach embedded in the 3D Eulerian model CHIMERE. However, the study also reveals comparable performances between the two approaches for the aforementioned pollutants in areas near the city center, where urban characteristics are more uniform.
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 31, 587–602, https://doi.org/10.5194/npg-31-587-2024, https://doi.org/10.5194/npg-31-587-2024, 2024
Short summary
Short summary
Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.
Jerry Jose, Auguste Gires, Ernani Schnorenberger, Yelva Roustan, Daniel Schertzer, and Ioulia Tchiguirinskaia
Nonlin. Processes Geophys., 31, 603–624, https://doi.org/10.5194/npg-31-603-2024, https://doi.org/10.5194/npg-31-603-2024, 2024
Short summary
Short summary
To understand the influence of rainfall on wind power production, turbine power and rainfall were measured simultaneously on an operational wind farm and analysed. The correlation between wind, wind power, air density, and other fields was obtained on various temporal scales under rainy and dry conditions. An increase in the correlation was observed with an increase in the rain; rain also influenced the correspondence between actual and expected values of power at various velocities.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
Short summary
Short summary
We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
Nonlin. Processes Geophys., 31, 335–357, https://doi.org/10.5194/npg-31-335-2024, https://doi.org/10.5194/npg-31-335-2024, 2024
Short summary
Short summary
A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
Short summary
Short summary
This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
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
Short summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
Short summary
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023, https://doi.org/10.5194/amt-16-1745-2023, 2023
Short summary
Short summary
Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, https://doi.org/10.5194/gmd-15-7641-2022, 2022
Short summary
Short summary
Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
Short summary
Short summary
This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Svetlana Tsyro, Wenche Aas, Augustin Colette, Camilla Andersson, Bertrand Bessagnet, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Kathleen Mar, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Valentin Raffort, Yelva Roustan, Mark R. Theobald, Marta G. Vivanco, Hilde Fagerli, Peter Wind, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, and Mario Adani
Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022, https://doi.org/10.5194/acp-22-7207-2022, 2022
Short summary
Short summary
Particulate matter (PM) air pollution causes adverse health effects. In Europe, the emissions caused by anthropogenic activities have been reduced in the last decades. To assess the efficiency of emission reductions in improving air quality, we have studied the evolution of PM pollution in Europe. Simulations with six air quality models and observational data indicate a decrease in PM concentrations by 10 % to 30 % across Europe from 2000 to 2010, which is mainly a result of emission reductions.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, https://doi.org/10.5194/acp-21-13247-2021, 2021
Short summary
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.
Cited articles
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
Bodin, T. and Sambridge, M.: Seismic tomography with the reversible jump
algorithm, Geophys. J. Int., 178, 1411–1436,
https://doi.org/10.1111/j.1365-246X.2009.04226.x, 2009. a, b, c, d
Chino, M., Nakayama, H., Nagai, H., Terada, H., Katata, G., and Yamazawa, H.:
Preliminary Estimation of Release Amounts of 131I and 137Cs
Accidentally Discharged from the Fukushima Daiichi Nuclear Power
Plant into the Atmosphere, J. Nucl. Sci. Technol, 48, 1129–1134,
https://doi.org/10.1080/18811248.2011.9711799, 2011. a, b
Delle Monache, L., Lundquist, J., Kosović, B., Johannesson, G., Dyer, K.,
Aines, R. D., Chow, F., Belles, R., Hanley, W., Larsen, S., Loosmore, G.,
Nitao, J., Sugiyama, G., and Vogt, P.: Bayesian inference and markov chain
monte carlo sampling to reconstruct a contaminant source on a continental
scale, J. Appl. Meteorol. Clim., 47, 2600–2613,
https://doi.org/10.1175/2008JAMC1766.1, 2008. a, b
Dumont Le Brazidec, J. and Saunier, O.: Statistics on caesium 137 deposition around the Fukushima-Daiichi plant after the 2011 accident, Zenodo [data set], https://doi.org/10.5281/zenodo.7016491, 2022. a
Dumont Le Brazidec, J., Bocquet, M., Saunier, O., and Roustan, Y.: Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event, Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, 2021. a, b, c, d, e, f, g, h, i
Dumont Le Brazidec, J., Bocquet, M., Saunier, O., and Roustan, Y.: Inverse Bayesian Inference for Source Assesment (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7318543, 2022. a
Furuta, S., Sumiya, S., Watanabe, H., Nakano, M., Imaizumi, K., Takeyasu, M.,
Nakada, A., Fujita, H., Mizutani, T., Morisawa, M., Kokubun, Y., Kono, T.,
Nagaoka, M., Yokoyama, H., Hokama, T., Isozaki, T., Nemoto, M., Hiyama, Y.,
Onuma, T., Kato, C., and Kurachi, T.: Results of the environmental radiation
monitoring following the accident at the Fukushima Daiichi Nuclear
Power Plant Interim report Ambient radiation dose rate, radioactivity
concentration in the air and radioactivity concentration in the fallout,
Tech. rep., Radiation Protection Department, Nuclear Fuel Cycle Engineering
Laboratories, Tokai Research and Development Center, Japan Atomic Energy
Agency, Japan, jAEA-Review–2011-035 INIS Reference Number: 43088311, OSTI ID: 21609259, 2011. a
Green, P. J.: Reversible jump Markov chain Monte Carlo computation and
Bayesian model determination, Biometrika, 82, 711–732,
https://doi.org/10.1093/biomet/82.4.711, 1995. a, b
Hastie, D. I. and Green, P. J.: Model choice using reversible jump Markov
chain Monte Carlo, Stat. Neerl., 66, 309–338,
https://doi.org/10.1111/j.1467-9574.2012.00516.x, 2012. a, b
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning: Data Mining, Inference, and Prediction, Second
Edition, Springer Series in Statistics, Springer-Verlag, New York, 2
edn., ISBN 978-0-387-84857-0, 2009. a
Hirao, S., Yamazawa, H., and Nagae, T.: Estimation of release rate of
iodine-131 and cesium-137 from the Fukushima Daiichi nuclear power plant,
J. Nucl. Sci. Technol., 50, 139–147, https://doi.org/10.1080/00223131.2013.757454, 2013. a
IAEA: The Fukushima Daiichi accident, Tech. rep., Vienna, 978-92-0-107015-9, 2015. a
Jeffreys, H.: An invariant form for the prior probability in estimation
problems, P. Roy. Soc. Lond. A Mat., 186, 453–461, https://doi.org/10.1098/rspa.1946.0056,
1946. a
Katata, G., Ota, M., Terada, H., Chino, M., and Nagai, H.: Atmospheric
discharge and dispersion of radionuclides during the Fukushima Dai-ichi
Nuclear Power Plant accident. Part I: Source term estimation and
local-scale atmospheric dispersion in early phase of the accident, J.
Environ. Radioact., 109, 103–113, https://doi.org/10.1016/j.jenvrad.2012.02.006, 2012. a
Katata, G., Chino, M., Kobayashi, T., Terada, H., Ota, M., Nagai, H., Kajino, M., Draxler, R., Hort, M. C., Malo, A., Torii, T., and Sanada, Y.: Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model, Atmos. Chem. Phys., 15, 1029–1070, https://doi.org/10.5194/acp-15-1029-2015, 2015. a, b
Keats, A., Yee, E., and Lien, F.-S.: Bayesian inference for source
determination with applications to a complex urban environment, Atmos.
Environ., 41, 465–479, https://doi.org/10.1016/j.atmosenv.2006.08.044, 2007. a
Kobayashi, T., Nagai, H., Chino, M., and Kawamura, H.: Source term estimation
of atmospheric release due to the Fukushima Dai-ichi Nuclear Power
Plant accident by atmospheric and oceanic dispersion simulations, J. Nucl.
Sci. Technol., 50, 255–264, https://doi.org/10.1080/00223131.2013.772449,
2013. a
Li, X., Sun, S., Hu, X., Huang, H., Li, H., Morino, Y., Wang, S., Yang, X.,
Shi, J., and Fang, S.: Source inversion of both long- and short-lived
radionuclide releases from the Fukushima Daiichi nuclear accident using
on-site gamma dose rates, J. Hazard. Mater., 379, 120770,
https://doi.org/10.1016/j.jhazmat.2019.120770, 2019. a
Liu, Y., Haussaire, J.-M., Bocquet, M., Roustan, Y., Saunier, O., and Mathieu,
A.: Uncertainty quantification of pollutant source retrieval: comparison of
Bayesian methods with application to the Chernobyl and Fukushima
Daiichi accidental releases of radionuclides, Q. J. Roy. Meteor. Soc., 143,
2886–2901, https://doi.org/10.1002/qj.3138, 2017. a, b, c, d, e, f, g, h, i, j, k, l
Louis, J.-F.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, https://doi.org/10.1007/BF00117978, 1979. a
Mathieu, A., Korsakissok, I., Quélo, D., Groëll, J., Tombette, M., Didier,
D., Quentric, E., Saunier, O., Benoit, J.-P., and Isnard, O.: Atmospheric
Dispersion and Deposition of Radionuclides from the Fukushima
Daiichi Nuclear Power Plant Accident, Elements, 8, 195–200,
https://doi.org/10.2113/gselements.8.3.195, 2012. a
MEXT: NRA: Results of the (i) Fifth Airborne Monitoring Survey and
(ii) Airborne Monitoring Survey Outside 80 km from the Fukushima
Dai-ichi NPP,
http://radioactivity.nsr.go.jp/en/contents/6000/5790/24/203_0928_14e.pdf (last access: 3 February 2023),
2012. a
Nagai, H., Terada, H., Tsuduki, K., Katata, G., Ota, M., Furuno, A., and Akari,
S.: Updating source term and atmospheric dispersion simulations for the dose
reconstruction in Fukushima Daiichi Nuclear Power Station
Accident, EPJ Web Conf., 153, 08012, https://doi.org/10.1051/epjconf/201715308012,
2017. a
Nagakawa, Y., Sotodate, T., Kinjo, Y., and Suzuki, T.: One-year time variations
of anthropogenic radionuclides in aerosols in Tokyo after the Fukushima
Dai-ichi Nuclear Power Plant reactor failures, J. Nucl. Sci. Technol.,
52, 784–791, https://doi.org/10.1080/00223131.2014.985279, 2015. a
Oura, Y., Ebihara, M., Tsuruta, H., Nakajima, T., Ohara, T., Ishimoto, M.,
Sawahata, H., Katsumura, Y., and Nitta, W.: A Database of Hourly
Atmospheric Concentrations of Radiocesium (134Cs and
137Cs) in Suspended Particulate Matter Collected in
March 2011 at 99 Air Pollution Monitoring Stations in Eastern
Japan, J. Nucl. Radiochem. Sci., 15, 2_1–2_12,
https://doi.org/10.14494/jnrs.15.2_1, 2015 (data available at: http://www.radiochem.org/en/j-online152.html, last access: 3 February 2023). a, b
Querel, A., Quelo, D., Roustan, Y., and Mathieu, A.: Sensitivity study to
select the wet deposition scheme in an operational atmospheric transport
model, J. Environ. Radioactiv., 237, 106712,
https://doi.org/10.1016/j.jenvrad.2021.106712, 2021. a
Quélo, D., Krysta, M., Bocquet, M., Isnard, O., Minier, Y., and Sportisse, B.:
Validation of the Polyphemus platform on the ETEX, Chernobyl and
Algeciras cases, Atmos. Environ., 41, 5300–5315,
https://doi.org/10.1016/j.atmosenv.2007.02.035, 2007. a
Robert, C. P., Elvira, V., Tawn, N., and Wu, C.: Accelerating MCMC
algorithms, Wiley Interdiscip. Rev. Comput. Stat., 10, e1435,
https://doi.org/10.1002/wics.1435, 2018. a
Saunier, O., Mathieu, A., Didier, D., Tombette, M., Quélo, D., Winiarek, V., and Bocquet, M.: An inverse modeling method to assess the source term of the Fukushima Nuclear Power Plant accident using gamma dose rate observations, Atmos. Chem. Phys., 13, 11403–11421, https://doi.org/10.5194/acp-13-11403-2013, 2013. a, b, c, d, e, f, g, h, i, j
Saunier, O., Didier, D., Mathieu, A., Masson, O., and Dumont Le Brazidec, J.:
Atmospheric modeling and source reconstruction of radioactive ruthenium from
an undeclared major release in 2017, P. Natl. Acad. Sci. USA, 116,
24991–25000, https://doi.org/10.1073/pnas.1907823116, 2019. a
Stohl, A., Seibert, P., Wotawa, G., Arnold, D., Burkhart, J. F., Eckhardt, S., Tapia, C., Vargas, A., and Yasunari, T. J.: Xenon-133 and caesium-137 releases into the atmosphere from the Fukushima Dai-ichi nuclear power plant: determination of the source term, atmospheric dispersion, and deposition, Atmos. Chem. Phys., 12, 2313–2343, https://doi.org/10.5194/acp-12-2313-2012, 2012. a
Takehisa, O., Tetsuya, O., Mitsumasa, T., Yukio, S., Masamitsu, K., Hitoshi,
A., Yasuaki, K., Masatsugu, K., Jun, S., Masahiro, T., Hitoshi, O., Shunsuke,
T., Shunsuke, T., Tadahiro, S., and Tadahiro, S.: Emergency Monitoring of
Environmental Radiation and Atmospheric Radionuclides at Nuclear
Science Research Institute, JAEA Following the Accident of
Fukushima Daiichi Nuclear Power Plant, JAEA-Data,
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201402276577269847 (last access: 3 Februaru 2023),
2012. a, b
Tanaka, K., Sakaguchi, A., Kanai, Y., Tsuruta, H., Shinohara, A., and
Takahashi, Y.: Heterogeneous distribution of radiocesium in aerosols, soil
and particulate matters emitted by the Fukushima Daiichi Nuclear
Power Plant accident: retention of micro-scale heterogeneity during the
migration of radiocesium from the air into ground and river systems, J. Radioanal. Nucl. Ch., 295, 1927–1937,
https://doi.org/10.1007/s10967-012-2160-9, 2013. a, b
Terada, H., Katata, G., Chino, M., and Nagai, H.: Atmospheric discharge and
dispersion of radionuclides during the Fukushima Dai-ichi Nuclear
Power Plant accident. Part II: verification of the source term and
analysis of regional-scale atmospheric dispersion, J. Environ. Radioact.,
112, 141–154, https://doi.org/10.1016/j.jenvrad.2012.05.023, 2012. a, b
Terada, H., Nagai, H., Tsuduki, K., Furuno, A., Kadowaki, M., and Kakefuda, T.:
Refinement of source term and atmospheric dispersion simulations of
radionuclides during the Fukushima Daiichi Nuclear Power Station
accident, J. Environ. Radioact., 213, 106104,
https://doi.org/10.1016/j.jenvrad.2019.106104, 2020. a, b, c, d, e, f, g, h, i
Tichý, O., Šmídl, V., Hofman, R., and Stohl, A.: LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination, Geosci. Model Dev., 9, 4297–4311, https://doi.org/10.5194/gmd-9-4297-2016, 2016. a
Troen, I. B. and Mahrt, L.: A simple model of the atmospheric boundary layer;
sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148,
https://doi.org/10.1007/BF00122760, 1986. a
Tsuruta, H., Oura, Y., Ebihara, M., Moriguchi, Y., Ohara, T., and Nakajima, T.:
Time-series analysis of atmospheric radiocesium at two SPM monitoring sites
near the Fukushima Daiichi Nuclear Power Plant just after the
Fukushima accident on March 11, 2011, Geochem. J., 52, 103–121,
https://doi.org/10.2343/geochemj.2.0520, 2018. a, b
Winiarek, V., Bocquet, M., Saunier, O., and Mathieu, A.: Estimation of errors
in the inverse modeling of accidental release of atmospheric pollutant:
Application to the reconstruction of the cesium-137 and iodine-131 source
terms from the Fukushima Daiichi power plant, J. Geophys. Res.-Atmos., 117, D05122, https://doi.org/10.1029/2011JD016932, 2012. a, b
Winiarek, V., Bocquet, M., Duhanyan, N., Roustan, Y., Saunier, O., and Mathieu,
A.: Estimation of the caesium-137 source term from the Fukushima Daiichi
nuclear power plant using a consistent joint assimilation of air
concentration and deposition observations, Atmos. Environ., 82, 268–279,
https://doi.org/10.1016/j.atmosenv.2013.10.017, 2014. a, b
Yamada, J., Seya, N., Haba, R., Muto, Y., Shimizu, T., Takasaki, K., Numari,
H., Sato, N., Nemoto, K., and Takasaki, H.: Environmental radiation
monitoring resulting from the accident at the Fukushima Daiichi Nuclear
Power Plant, conducted by: Oarai Research and Development Center,
JAEA Results of ambient gamma-ray dose rate, atmospheric radioactivity
and meteorological observation, Tech. Rep. Code–2013-006 INIS Reference
Number: 48050473, Oarai Research and Development Center, Japan Atomic Energy
Agency, Japan, Tech. Rep. Code-2013-006 INIS Reference Number: 48050473, 2013 (in Japanese). a, b
Yee, E.: Theory for Reconstruction of an Unknown Number of Contaminant
Sources using Probabilistic Inference, Bound.-Lay. Meteorol., 127,
359–394, https://doi.org/10.1007/s10546-008-9270-5, 2008. a
Yee, E., Hoffman, I., and Ungar, K.: Bayesian Inference for Source
Reconstruction: A Real-World Application, Int. Sch. Res. Notices,
2014, 507634, https://doi.org/10.1155/2014/507634, 2014. a
Yumimoto, K., Morino, Y., Ohara, T., Oura, Y., Ebihara, M., Tsuruta, H., and
Nakajima, T.: Inverse modeling of the 137Cs source term of the Fukushima
Dai-ichi Nuclear Power Plant accident constrained by a deposition map
monitored by aircraft, J. Environ. Radioact., 164, 1–12,
https://doi.org/10.1016/j.jenvrad.2016.06.018, 2016. a, b
Short summary
When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
When radionuclides are released into the atmosphere, the assessment of the consequences depends...
