Potential of the International Monitoring System radionuclide network for inverse modelling

The International Monitoring System (IMS) radionuclide network enforces the Comprehensive NuclearTest-Ban Treaty which bans nuclear explosions. We have evaluated the potential of the IMS radionuclide network for inverse modelling of the source, whereas it is usually assessed by its detection capability. To do so, we have chosen the degrees of freedom for the signal (DFS), a well established criterion in remote sensing, in order to assess the performance of an inverse modelling system. Using a recent multiscale data assimilation technique, we have computed optimal adaptive grids of the source parameter space by maximising the DFS. This optimisation takes into account the monitoring network, the meteorology over one year (2009) and the relationship between the source parameters and the observations derived from the FLEXPART Lagrangian transport model. Areas of the domain where the grid-cells of the optimal adaptive grid are large emphasise zones where the retrieval is more uncertain, whereas areas where the grid-cells are smaller and denser stress regions where more source variables can be resolved. The observability of the globe through inverse modelling is studied in strong, realistic and small model error cases. The strong error and realistic error cases yield heterogeneous adaptive grids, indicating that information does not propagate far from the monitoring stations, whereas in the small error case, the grid is much more homogeneous. In all cases, several specific continental regions remain poorly observed such as Africa as well as the tropics, because of the trade winds. The northern hemisphere is better observed through inverse modelling (more than 60% of the total DFS) mostly because it contains more IMS stations. This unbalance leads to a better performance of inverse modelling in the northern hemisphere winter. The methodology is also applied to the subnetwork composed of the stations of the IMS network which measure noble gases.

[1]  Marc Bocquet,et al.  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 INVERSE MODELING , 2012 .

[2]  Emil M. Constantinescu,et al.  Modeling atmospheric chemistry and transport with dynamic adaptive resolution , 2008 .

[3]  Marc Bocquet,et al.  Source reconstruction of an accidental radionuclide release at European scale , 2007 .

[4]  A. Stohl,et al.  Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2 , 2005 .

[5]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[6]  Marc Bocquet,et al.  Constraining surface emissions of air pollutants using inverse modelling: method intercomparison and a new two-step two-scale regularization approach , 2011 .

[7]  Marc Bocquet,et al.  Probing ETEX-II data set with inverse modelling , 2008 .

[8]  Gerhard Wotawa,et al.  Backtracking of Noble Gas Measurements Taken in the Aftermath of the Announced October 2006 Event in North Korea by Means of PTS Methods in Nuclear Source Estimation and Reconstruction , 2010 .

[9]  Marc Bocquet,et al.  Design of a monitoring network over France in case of a radiological accidental release , 2008 .

[10]  Marc Bocquet,et al.  Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release , 2011 .

[11]  Marc Bocquet,et al.  Parameter‐field estimation for atmospheric dispersion: application to the Chernobyl accident using 4D‐Var , 2012 .

[12]  J.-P. Issartel,et al.  Inverse transport for the verification of the Comprehensive Nuclear Test Ban Treaty , 2003 .

[13]  岩崎 民子 SOURCES AND EFFECTS OF IONIZING RADIATION : United Nations Scientific Committee on the Effects of Atomic Radiation UNSCEAR 2000 Report to the General Assembly, with Scientific Annexes , 2002 .

[14]  Frédéric Hourdin,et al.  Sub‐surface nuclear tests monitoring through the CTBT Xenon Network , 2000 .

[15]  Marc Bocquet,et al.  Reconstruction of an atmospheric tracer source using the principle of maximum entropy. I: Theory , 2005 .

[16]  F. Girardi,et al.  The field campaigns of the European Tracer Experiment (ETEX): overview and results , 1998 .

[17]  Lin Wu,et al.  Optimal representation of source‐sink fluxes for mesoscale carbon dioxide inversion with synthetic data , 2011 .

[18]  C. Vogel Computational Methods for Inverse Problems , 1987 .

[19]  Marc Bocquet Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation , 2009 .

[20]  Marc Bocquet,et al.  Targeting of observations for accidental atmospheric release monitoring , 2009 .

[21]  Marc Bocquet,et al.  Validation of the Polyphemus platform on the ETEX, Chernobyl and Algeciras cases , 2007 .

[22]  Marc Bocquet Reconstruction of an atmospheric tracer source using the principle of maximum entropy. II: Applications , 2005 .

[23]  Marc Bocquet,et al.  Bayesian design of control space for optimal assimilation of observations. Part II: Asymptotic solutions , 2011 .

[24]  Gerhard Wotawa,et al.  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 , 2011 .

[25]  Lin Wu,et al.  Optimal redistribution of the background ozone monitoring stations over France , 2011 .

[26]  Lin Wu,et al.  Bayesian design of control space for optimal assimilation of observations. Part I: Consistent multiscale formalism , 2011 .

[27]  Luca Delle Monache,et al.  Bayesian Inference and Markov Chain Monte Carlo Sampling to Reconstruct a Contaminant Source on a Continental Scale , 2008 .

[28]  Janusz A. Pudykiewicz,et al.  APPLICATION OF ADJOINT TRACER TRANSPORT EQUATIONS FOR EVALUATING SOURCE PARAMETERS , 1998 .

[29]  Martin Kalinowski,et al.  Computation and Analysis of the Global Distribution of the Radioxenon Isotope 133Xe based on Emissions from Nuclear Power Plants and Radioisotope Production Facilities and its Relevance for the Verification of the Nuclear-Test-Ban Treaty , 2010 .

[30]  Gerhard Wotawa,et al.  A long distance measurement of radioxenon in Yellowknife, Canada, in late October 2006 , 2007 .

[31]  M. Bocquet,et al.  Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation , 2010 .

[32]  Marc Bocquet,et al.  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 , 2012 .

[33]  Fue-Sang Lien,et al.  Bayesian inversion of concentration data: Source reconstruction in the adjoint representation of atmospheric diffusion , 2008 .

[34]  Gerhard Wotawa,et al.  Atmospheric transport modelling in support of CTBT verification—overview and basic concepts , 2003 .

[35]  P. Hansen Discrete Inverse Problems: Insight and Algorithms , 2010 .

[36]  Lennart Robertson,et al.  Bayesian updating of atmospheric dispersion after a nuclear accident , 2004 .

[37]  R. Prinn,et al.  Inversion of long-lived trace gas emissions using combined Eulerian and Lagrangian chemical transport models , 2011 .

[38]  Marc Bocquet High‐resolution reconstruction of a tracer dispersion event: application to ETEX , 2007 .

[39]  Marc Bocquet,et al.  Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport , 2007 .

[40]  Joakim Langner,et al.  Source function estimate by means of variational data assimilation applied to the ETEX-I tracer experiment , 1998 .