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

Winiarek, Victor; Bocquet, Marc; Saunier, Olivier; Mathieu, Anne

doi:10.1029/2011jd016932

Cited by 97 publications

(138 citation statements)

References 45 publications

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“…Most of the cited works empirically assigned these error statistics. Objective methods of tuning the errors in the system also exist (Wahba et al, 1994;Dee, 1995;Desroziers and Ivanov, 2001) and have been applied to get the general structure of the errors (Michalak et al, 2005;Winiarek et al, 2012). But these methods rely on subjective prior knowledge on the error structure (e.g., isotropic spatial correlation or temporal decay in the correlations), which can limit the generality of the results.…”

Section: A Berchet Et Al: Error Statistics For Atmospheric Inversionmentioning

confidence: 99%

Towards better error statistics for atmospheric inversions of methane surface fluxes

et al. 2013

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We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared

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Section: A Berchet Et Al: Error Statistics For Atmospheric Inversionmentioning

confidence: 99%

Towards better error statistics for atmospheric inversions of methane surface fluxes

et al. 2013

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“…Recent studies inquired into objectified ways of specifying these matrices (e.g. Michalak and Kitanidis, 2005;Winiarek et al, 2012;Berchet et al, 2013b). The approach in these papers was to find optimal uncertainty matrices R and B along an objective statistical criterion: the maximum likelihood.…”

Section: Motivations Towards Marginalizingmentioning

confidence: 99%

“…in order to evaluate their impact on the inversion results (Lauvaux et al, 2009;Winiarek et al, 2012;Berchet et al, 2013b;Ganesan et al, 2014). In Berchet et al (2015), we proposed a general method in order to objectively quantify most of the critical sources of errors in the inversion.…”

Section: A Berchet Et Al: Ch 4 Flux In Eurasia 5395mentioning

confidence: 99%

Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion

et al. 2015

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Abstract. Eight surface observation sites providing quasicontinuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate 1 year of these in situ observations in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the mesoscale in the Siberian lowlands for the year 2010. To do so, we first inquire about the way the inversion uses the observations and the way the fluxes are constrained by the observation sites. As atmospheric inversions at the mesoscale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversion systems. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution quasicontinuous signal is representative of very local emission patterns difficult to analyse with a mesoscale system. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites than the ones currently in operation are then necessary to constrain the whole Siberian lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. Finally, the tolerance intervals for posterior methane fluxes are of roughly 20 % (resp. 50 %) of the fluxes for anthropogenic (resp. wetland) sources. About 50-70 % of Siberian lowlands emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian lowlands, we find a regional methane budget of 5-28 TgCH 4 for the year 2010, i.e. 1-5 % of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are tentatively used for the evaluation of the inversion results, but they exhibit only a marginal signal from the fluxes within the region of interest.

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“…Chino et al, 2011;Katata et al, 2012;Terada et al, 2012;Hirao et al, 2013;Kobayashi et al, 2013;Oza et al, 2013; 15 Katata et al, 2015;Achim et al, 2014) to those sophisticated ones using various dispersion models and inverse modeling schemes (e.g. Stohl et al, 2012;Winiarek et al, 2012;Saunier et al, 2013;Winiarek et al, 2014;Chai et al, 2015). Another active field for STE applications is the estimation of the volcanic ash emissions.…”

Section: Introductionmentioning

confidence: 99%

“…Reanalysis (NARR) at 32-km (Mesinger et al, 2006 To avoid running the HYSPLIT modeling repeatedly, a TCM is generated similar to the previous HYSPLIT inverse modeling 5 studies (Chai et al, 2015(Chai et al, , 2017. As described in Draxler and Rolph (2012), independent simulations are performed with a unit emission rate from each source location and a pre-defined time segment.…”

mentioning

confidence: 99%

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

Chai¹,

Stein²,

Ngan³

2018

Preprint

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Abstract.A HYSPLIT inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2 for which the HYSPLIT has the best performance. Before introducing model uncertainty terms, the tests using concentration differences in the cost function results in severe underestimation while those using logarithm concentrations differences results in overestimation of the release rate. Adding model uncertainty 5 terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yield better results than calculating concentration differences and the estimates are more robust for a reasonable range of model uncertainty parameters. This is 10 further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters and the largest relative error among the six releases is 53.3%. At last, the system is tested for its capability to find a 15 single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all the six releases. However, the estimated location may have large errors. 1Geosci. Model Dev. Discuss., https://doi

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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

Cited by 97 publications

References 45 publications

Towards better error statistics for atmospheric inversions of methane surface fluxes

Towards better error statistics for atmospheric inversions of methane surface fluxes

Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

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