Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.
A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling.
Institute of Information Theory and Automation of the Czech Academy of Sciences, v.v.i., Pod Vodarenskou vezi 4, 182 08, Prague 8, Czech Republic. Electronic address: pecha@utia.cas.cz.
Petr
P
Pecha
Institute of Information Theory and Automation of the Czech Academy of Sciences, v.v.i., Pod Vodarenskou vezi 4, 182 08, Prague 8, Czech Republic.
Václav
V
Šmídl
D000465:Algorithms; D003198:Computer Simulation; D005544:Forecasting; D008962:Models, Theoretical; D016011:Normal Distribution; D011834:Radiation Monitoring / Q000379:methods*; D018788:Radioactive Hazard Release; D011851:Radioactivity
Assimilation of measurements; Ill-posed inversion problem; Measurement noise; Radioactivity release; Urgent emergency