Data assimilation in the decision support system RODOS

Abstract

Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.

Guidance on monitoring and data assimilation

“…A few of them have to some extent been utilised, for example, in the RODOS system. These include Kalman filtering (Rojas-Palma et al, 2003;Astrup et al, 2004), probability-based Bayesian analysis (French and Smith, 1997) and a method utilising so-called location factors Kaiser and Pröhl, 2007). Each method has its advantages and drawbacks.…”

Data Assimilation of Model Predictions of Long-Time Evolution of Cs-137 Deposition on Terrain

“…The partitioning has physical background. Q (1) concerns the uncertainty of forecast model parameters introduced in Section 3. This component is found as a covariance of sample obtained via Monte-Carlo simulation with many different model settings.…”

“…The overall covariance is scaled with α. This parametrization allows mutual scaling of unstructured noise component Q (1) given upon numerical simulation and "additional" structured noise given by Q (2) . For finding the most plausible values of θ similar approach as proposed in [8] or [9] based on modeled-minus-observed residuals is used.…”

“…Along with this trend, demands of responsible authorities on systems for modeling of eventual consequences of possible nuclear accidents increase [1]. Research team from UTIA participated on customization of the RODOS (Rael-time On-line DecisiOn support System) [2] on the Czech conditions.…”

Modelling the radionuclide contamination of the Black Sea in the result of Chernobyl accident using circulation model and data assimilation

“…The use of measured data in the calculations of prognostic models is known as the problem of data assimilation. In the frames of the EU DAONEM project data assimilation tools were developed for the most of RODOS prognostic models, including the models of the hydrological model chain [4]. A set of specific constraints for the data assimilation in real-time system [4] were the main factors in choosing the particular method of data assimilation.…”