Assimilation of observations of radiation level into an atmospheric transport model: A case study with the particle filter and the ETEX tracer dataset

Atmospheric transport models and observations from monitoring networks are commonly used aids for forecasting spatial distribution of contamination in case of a radiological incident. In this study, we assessed the particle filter data-assimilation technique as a tool for ensemble forecasting the spread of radioactivity. We used measurements from the ETEX-1 tracer experiment and model results from the NPK-Puff atmospheric dispersion model. We showed that assimilation of observations improves the ensemble forecast compared to runs without data assimilation. The improvement is most prominent for nowcasting: the mean squared error was reduced by a factor of 7. For forecasting, the improvement of the mean squared error resulting from assimilation of observations was found to dissipate within a few hours. We ranked absolute model values and observations and calculated the mean squared error of the ranked values. This measure of the correctness of the pattern of high and low values showed an improvement for forecasting up to 48 h. We conclude that the particle filter is an effective tool in better modeling the spread of radioactivity following a release.

► We used the particle filter to model the ETEX tracer dataset. ► The particle filter is successful in modeling the pattern of tracer concentration. ► The particle filter is less successful in modeling absolute concentration values. ► The particle filter is an effective tool for modeling the spread of radioactivity.