(A) study on the weather sampling method for probabilistic consequence analysis

Abstract

The main task of probabilistic accident consequence analysis model is to predict the radiological situation and to provide a reliable quantitative data base for making decisions on countermeasures. The magnitude of accident consequence is depended on the characteristic of the accident and the weather coincident. In probabilistic accident consequence analysis, it is necessary to repeat the atmoshperic dispersion calculation with several hundreds of weather sequences to predict the full distribution of consequences which may occur following a postulated accident release. It is desirable to select a representative sample of weather sequences from a meteolorogical record which is typical of the area over which the released radionuclides will disperse and which spans a sufficiently long period. The selection process is done by means of sampling techniques from a full year of hourly weather data characteristic of the plant site. In this study, the proposed Weighted importance sampling method selects proportional to the each bin size to closely approximate the true frequency distribution of weather condition at the site. The Weighted importance sampling method results in substantially less sampling uncertainty than the previous technique. The proposed technique can result in improve confidence in risk estimates.