Guidance on monitoring and data assimilation

Abstract

Decision makers must react in a prompt and appropriate manner in various emergency situations. The bases for decisions are often predictions produced with decision support systems (DSS). Actual radiation measurement data can be used to improve the reliability of the predictions. Data assimilation is an important link between model calculations and measurements and thus decreases the overall uncertainty of the DSS predictions. However, different aspects have to be taken into account for the optimal use of the data assimilation technique: different countries may have differing measurement strategies and systems as well as differing calculation models. The scenario and the amount and composition of radionuclides released may vary. In this paper we analyse the situation during and after an accident and draw up a list of recommendations that can help modellers to take into account the measurements that are best suited for data assimilation.

Operationalising an ensemble approach in the description of uncertainty in atmospheric dispersion modelling and an emergency response

“…Uncertainty tends to be greatest during the pre-release phase, in part because measurements are not yet available. This is in contrast to the post-release phase, when measurements can be used to reduce model uncertainty (Lahtinen et al, 2010;Bleher et al, 2020). Mathieu et al (2018) presented guidelines ranking uncertainties for atmospheric dispersion modelling.…”