Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

Winiarek, Victor; Vira, Julius; Bocquet, Marc; Sofiev, M.; Saunier, Olivier

doi:10.1016/j.atmosenv.2010.12.025

Cited by 39 publications

(24 citation statements)

References 27 publications

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“…Besides, as mentioned in the introduction, it is assumed they have the same variance, so that

boldR = r^{2} I_{d}

(a property called homoscedasticity). More sophisticated approaches are possible [ Abida and Bocquet , 2009; Winiarek et al , 2011]. However, the latter necessarily rely on more sophisticated models for the statistics of the observation errors.…”

Section: Methodology: Modeling Errorsmentioning

confidence: 99%

“…However, the simulations run over a longer period (from 11 March to 5 April) in order to exploit the information content of later observations. A total of 384 direct simulations are performed to fill the Jacobian matrix H column by column, and no adjoint model is needed in this process [see Winiarek et al , 2011]. The meteorological fields used in this study are the fields computed by the operational model from the European Centre for Medium‐Range Weather Forecasts (ECMWF).…”

Section: Application To the Fukushima Daiichi Accidentmentioning

confidence: 99%

“…From Figure 5, it is clear that the number of observations is not sufficient to constrain the solution to be positive for most parameters. A larger number of observations would have probably helped in that respect as shown by Winiarek et al [2011]. Consequently, in the absence of a large number of observations, non‐Gaussian statistical assumptions that constrain the source to be positive are paramount.…”

Section: Application To the Fukushima Daiichi Accidentmentioning

confidence: 99%

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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

Winiarek¹,

Bocquet²,

Saunier³

et al. 2012

J. Geophys. Res.

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126

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[1] A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativity of the measurements, those that are instrumental, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. We propose to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We apply the method to the estimation of the Fukushima Daiichi source term using activity concentrations in the air. The results are compared to an L-curve estimation technique and to Desroziers's scheme. The total reconstructed activities significantly depend on the chosen method. Because of the poor observability of the Fukushima Daiichi emissions, these methods provide lower bounds for cesium-137 and iodine-131 reconstructed activities. These lower bound estimates, 1.2 Â 10 16 Bq for cesium-137, with an estimated standard deviation range of 15%-20%, and 1.9 À 3.8 Â 10 17 Bq for iodine-131, with an estimated standard deviation range of 5%-10%, are of the same order of magnitude as those provided by the Japanese Nuclear and Industrial Safety Agency and about 5 to 10 times less than the Chernobyl atmospheric releases.

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“…Besides, as mentioned in the introduction, it is assumed they have the same variance, so that

boldR = r^{2} I_{d}

Section: Methodology: Modeling Errorsmentioning

confidence: 99%

Section: Application To the Fukushima Daiichi Accidentmentioning

confidence: 99%

Section: Application To the Fukushima Daiichi Accidentmentioning

confidence: 99%

See 1 more Smart Citation

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

Winiarek¹,

Bocquet²,

Saunier³

et al. 2012

J. Geophys. Res.

Self Cite

126

View full text Add to dashboard Cite

show abstract

“…Source inversion algorithms have been successfully applied for nuclear accidents (e.g. Winiarek et al, 2011;Saunier et al, 2013). However, the automation and usage of such algorithms in real-time is still at its preliminary stage.…”

Section: Introductionmentioning

confidence: 99%

“…However, the automation and usage of such algorithms in real-time is still at its preliminary stage. Winiarek et al (2011) developed automated algorithms for near-real-time source estimation of radioactive release based on the data of the network of concentration measurements in France. However, the most reliably available measurements in the vicinity of nuclear power plants are gamma-dose rates (GDR).…”

Section: Introductionmentioning

confidence: 99%

Advanced method for source term estimation and status of its integration in JRODOS

et al. 2016

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Estimating model evidence using data assimilation

Carrassi¹,

Bocquet

Hannart

et al. 2017

Quart J Royal Meteoro Soc

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We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model -which corresponds, to the best of the modeler's knowledge, to the situation in the actual world in which a sequence of events has occurred -and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble-DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four-dimensional variational smoother (En-4D-Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time-dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three-variable convection model (L63), and (ii) the Lorenz 40-variable mid-latitude atmospheric dynamics model (L95). The numerical results of these three DA-based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA-based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution.

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Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

Cited by 39 publications

References 27 publications

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

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

Advanced method for source term estimation and status of its integration in JRODOS

Estimating model evidence using data assimilation

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