首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter
Authors:DQ Zheng  JKC Leung  BY Lee
Institution:1. Department of Physics, Jinan University, Guang Zhou, China;2. Department of Physics, The University of Hong Kong, Hong Kong, China;3. Hong Kong Observatory, Hong Kong,China;1. Institute of Atmospheric Physics, Czech Academy of Sciences, Department of Aeronomy, Czech Republic;2. Bulgarian Academy of Sciences, Department of Space Weather, Bulgaria;3. University of New Brunsvick, Faculty of Science, Canada;4. George Mason University, Department of Physics and Astronomy, Space Weather Lab, USA;1. Department of Nuclear Energy Science and Engineering, Naval University of Engineering, Wuhan 430033, China;2. Institute of Thermal Science and Power Engineering, Naval University of Engineering, Wuhan 430033, China
Abstract:For an atmospheric dispersion model designed for the assessment of nuclear accident consequences, some uncertain model parameters, such as source term and weather conditions, may influence the reliability of model predictions. In this respect, good estimations of both model state and uncertain parameters are required. In this paper, an ensemble Kalman filter (EnKF) based method for simultaneous state and parameter estimation, using off-site radiation monitoring data, is presented. This method is based on a stochastic state space model, which resembles the parameter errors with stochastic quantities. Three imperfect parameters, including the source release rate, wind direction and turbulence intensity were perturbed simultaneously, and multiple parameter estimation were performed. Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters. The estimated parameters given by EnKF nicely converge to the true values, and the method also tracks the temporal variation of those parameters.
Keywords:
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号