Radioxenon Time Series and Meteorological Pattern Analysis for CTBT Event Categorisation

Abstract

Understanding radioxenon time series and being able to distinguish anthropogenic from nuclear explosion signals are fundamental issues for the technical verification of the Comprehensive Nuclear-Test-Ban Treaty. Every radioxenon event categorisation methodology must take into account the background at each monitoring site to uncover anomalies that may be related to nuclear explosions. Feedback induced by local meteorological patterns on the equipment and on the sampling procedures has been included in the analysis to improve a possible event categorisation scheme. The occurrence probability of radioxenon outliers has been estimated with a time series approach characterising and avoiding the influence of local meteorological patterns. A power spectrum estimator for radioxenon and meteorological time series was selected; the randomness of the radioxenon residual time series has been tested for white noise by Kolmogorov-Smirnov and Ljung-Box tests. This methodological approach was applied to radioxenon data collected at two monitoring sites located at St. John's, Canada and Charlottesville, USA, equipped with two different noble gas systems. It shows different feedback with local meteorological patterns and randomness for the radioxenon data recorded at the selected sites of St. John's and Charlottesville as well as a different occurrence probability of the outliers in the normalized radioxenon original and residual time series

Atmospheric transport modeling based estimation of radioactive release from the Fukushima Dai-ichi nuclear power plant accident

“…Absolute calculations involving ATM usually have an uncertainty of one order of magnitude. It has also been discussed in recent literature [6] that local atmospheric patterns that are not resolved by the simulation can lead to altered signals;…”

Estimation of the time-dependent radioactive source-term from the Fukushima nuclear power plant accident using atmospheric transport modelling

“…In the case of the FD-NPP and the Japanese IMS station the distance between source and receptor is only about 250 km, which puts them on neighbouring points of the global 1 Â 1 grid. Therefore, local weather patterns, which are not resolved by the simulation software can be responsible for the transport of higher amounts of airborne material from the source to the receptor (Plastino et al, 2010). Naturally, this would result in measuring a concentration which is higher than the estimated one, even if the estimation would be based on a real source term, which has not been constant in time.…”

“…If now the emission of a source or the concentration at the receptor is known, the other parameter can be estimated via ATM. Of course, the results strongly depend on the meteorological conditions during the transport period, as well as on local atmospheric patterns that are not resolved by the simulation, which can lead to altered signals (Plastino et al, 2010). It is important to keep in mind that particles emitted in one certain time interval can contribute in multiple times and at different arrival times to the concentration at the receptor site via various trajectories through the atmosphere.…”

“…The CTBTO also utilized this package to monitor emissions from nuclear weapon tests (Wotawa et al, 2003;Zähringer et al, 2009;Plastino et al, 2010). It is desirable to run multiple FLEXPART jobs in order to calculate SRS fields for a number of different sources and receptors.…”

Impact of Monthly Radioxenon Source Time-Resolution on Atmospheric Concentration Predictions

“…If the emission of the source or the diluted concentration at the receptor is known, the other one can be estimated with the SRS value as calculated by ATM. Of course, the results strongly depend on the meteorological conditions of the regarded time period, as well as on local atmospheric patterns that are not resolved by the simulation, which can lead to altered signals (PLASTINO et al, 2010). Particles, which have been emitted in one time interval, can contribute at different arrival times to the concentration at the receptor site, via their various trajectories through the atmosphere.…”