Sensitivity study to select the wet deposition scheme in an operational atmospheric transport model.

The ability of operational atmospheric transport models to simulate the soil contamination caused by deposition processes is important in the response to a nuclear crisis. The Fukushima accident was characterized by wet deposition of Cs-137, which is difficult to simulate accurately based on observations. A sensitivity study investigated seven wet deposition schemes integrated into operational atmospheric transport models. Deposition maps produced from the multiple simulations are compared with each other and with the observed deposition. Similarities and discrepancies in average behavior are presented for a number of modeling cases on the basis of criteria representing soil contamination crisis management needs. This study confirms the importance of the wet deposition scheme in a crisis management context. None of the schemes used in the study are the best option to satisfy all the comparison criteria. This study suggests that crisis managers must not exclusively trust a single model for selecting responses. At the current time, it is preferable to use several wet deposition schemes in the modelling tools for emergency responses.

International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations.

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

Data assimilation for short range atmospheric dispersion of radionuclides: a case study of second-order sensitivity.

This article presents the application of variational data assimilation to a simple Gaussian plume model for radionuclides. Adjoint modeling is applied to the model in order to minimize discrepancies between contamination observations and model outputs. The interest of such an approach is to get a better estimation of some parameters such as emissions or dispersion parameters. A second-order analysis is also performed to assess the sensitivity of the optimized parameters to some poorly known parameters. Sensitivity with respect to network design is also done.