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Abstract: Inverse modelling techniques can be used to estimate the amount of radionuclides and the temporal profile of the source term released in the atmosphere during the accident of the Fukushima Daiichi nuclear power plant in March 2011. In Winiarek et al. (2012b), the lower bounds of the caesium-137 and iodine-131 source terms were estimated with such techniques, using activity concentration measurements. The importance of an objective assessment of prior errors (the observation errors and the background errors) was emphasised for a reliable inversion. In such critical context where the meteorological conditions can make the source term partly unobservable and where only a few observations are available, such prior estimation techniques are mandatory, the retrieved source term being very sensitive to this estimation. We propose to extend the use of these techniques to the estimation of prior errors when assimilating observations from several data sets. The aim is to compute an estimate of the caesium-137 source term jointly using all available data about this radionuclide, such as activity concentrations in the air, but also daily fallout measurements and total cumulated fallout measurements. It is crucial to properly and simultaneously estimate the background errors and the prior errors relative to each data set. A proper estimation of prior errors is also a necessary condition to reliably estimate the a posteriori uncertainty of the estimated source term. Using such techniques, we retrieve a total released quantity of caesium-137 in the interval 11.6−19.3 PBq with an estimated standard deviation range of 15−20% depending on the method and the data sets. The “blind” time intervals of the source term have also been strongly mitigated compared to the first estimations with only activity concentration data. [Copyright &y& Elsevier]

Abstract: Ozone is a harmful air pollutant at ground level, and its concentrations are routinely measured with monitoring networks. The network design problem aims at determining the optimal positioning of the monitoring stations. In this study, the background stations of the French routine pollution monitoring network (BDQA) are partially redistributed over France under a set of design objectives. These background stations report ozone variations at large spatial scale comparable with that of a chemistry-transport model (CTM). The design criterion needs to be defined on a regular grid that covers France, where in general no ozone observations are available for validation. Geostatistical ozone estimation methods are used to extrapolate concentrations to these grid nodes. The geostatistical criteria are introduced to minimize the theoretical error of those geostatistical extrapolations. A physical criterion is also introduced to measure the ability of a network to represent a physical ozone field retrieved from CTM simulations using geostatistical extrapolation methods. A third type of criteria of geometrical nature, e.g. a maximal coverage of the design domain, are based uniquely on the distance between the network stations. To complete the network design methodology, a stochastic optimization method, simulated annealing, is employed in the algorithm to select optimally the stations. Significant improvement with all the proposed criteria has been found for the optimally redistributed network against the original background BDQA network. For instance, the relative improvements in the physical criterion value range from 21% to 32% compared to randomly relocated networks. Different design criteria lead to different optimally relocated networks. The optimal networks under physical criteria are the most heterogeneously distributed. More background stations are displaced to the coast, frontiers, and large urban agglomerations, e.g. Paris and Marseilles. The ozone heterogeneous fields are not as well reconstructed from optimal networks under geostatistical or geometrical criteria as from the optimal network obtained with the physical criterion. The values of the physical criterion for the geostatistically and geometrically optimal networks show deteriorations of about 8% and 17% respectively compared to that of the physically optimal network. [Copyright &y& Elsevier]

Abstract: Ozone is a harmful air pollutant at ground level, and its concentrations are measured with routine monitoring networks. Due to the heterogeneous nature of ozone fields, the spatial distribution of the ozone concentration measurements is very important. Therefore, the evaluation of distributed monitoring networks is of both theoretical and practical interests. In this study, we assess the efficiency of the ozone monitoring network over France (BDQA) by investigating a network reduction problem. We examine how well a subset of the BDQA network can represent the full network. The performance of a subnetwork is taken to be the root mean square error (rmse) of the hourly ozone mean concentration estimations over the whole network given the observations from that subnetwork. Spatial interpolations are conducted for the ozone estimation taking into account the spatial correlations. Several interpolation methods, namely ordinary kriging, simple kriging, kriging about the means, and consistent kriging about the means, are compared for a reliable estimation. Exponential models are employed for the spatial correlations. It is found that the statistical information about the means improves significantly the kriging results, and that it is necessary to consider the correlation model to be hourly-varying and daily stationary. The network reduction problem is solved using a simulated annealing algorithm. Significant improvements can be obtained through these optimizations. For instance, removing optimally half the stations leads to an estimation error of the order of the standard observational error (10 μg m−3). The resulting optimal subnetworks are dense in urban agglomerations around Paris (Île-de-France) and Nice (Côte d’Azur), where high ozone concentrations and strong heterogeneity are observed. The optimal subnetworks are probably dense near frontiers because beyond these frontiers there is no observation to reduce the uncertainty of the ozone field. For large rural regions, the stations are uniformly distributed. The fractions between urban, suburban and rural stations are rather constant for optimal subnetworks of larger size (beyond 100 stations). By contrast, for smaller subnetworks, the urban stations dominate. [Copyright &y& Elsevier]

In geophysical data assimilation, observations shed light on a control parameter space through a model, a statistical prior, and an optimal combination of these sources of information. This control space can be a set of discrete parameters, or, more often in geophysics, part of the state space, which is distributed in space and time. When the control space is continuous, it must be discretized for numerical modeling. This discretization, in this paper called a representation of this distributed parameter space, is always fixed a priori. In this paper, the representation of the control space is considered a degree of freedom on its own. The goal of the paper is to demonstrate that one could optimize it to perform data assimilation in optimal conditions. The optimal representation is then chosen over a large dictionary of adaptive grid representations involving several space and time scales. First, to motivate the importance of the representation choice, this paper discusses the impact of a change of representation on the posterior analysis of data assimilation and its connection to the reduction of uncertainty. It is stressed that in some circumstances (atmospheric chemistry, in particular) the choice of a proper representation of the control space is essential to set the data assimilation statistical framework properly. A possible mathematical framework is then proposed for multiscale data assimilation. To keep the developments simple, a measure of the reduction of uncertainty is chosen as a very simple optimality criterion. Using this criterion, a cost function is built to select the optimal representation. It is a function of the control space representation itself. A regularization of this cost function, based on a statistical mechanical analogy, guarantees the existence of a solution. This allows numerical optimization to be performed on the representation of control space. The formalism is then applied to the inverse modeling of an accidental release of an atmospheric contaminant at European scale, using real data. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this article is to investigate the validity of a modeling system developed for forecasting atmospheric dispersion, the Polyphemus platform, with a special focus on radionuclides. The platform is briefly described and model-to-data comparisons are reported for three cases: the ETEX campaign, the Chernobyl accident and the Algeciras release. The results are similar to those usually given in the literature by state-of-the-art models. Some preliminary sensitivity analysis indicates the main sources for uncertainties. [Copyright &y& Elsevier]