期刊
JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER
卷 112, 期 3, 页码 434-447出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jqsrt.2010.10.005
关键词
Monte Carlo; Variance reduction; Atmosphere; Radiative transfer; Clouds
We present five new variance reduction techniques applicable to Monte Carlo simulations of radiative transfer in the atmosphere: detector directional importance sampling, n-tuple local estimate, prediction-based splitting and Russian roulette, and circum-solar virtual importance sampling. With this set of methods it is possible to simulate remote sensing instruments accurately and quickly. In contrast to all other known techniques used to accelerate Monte Carlo simulations in cloudy atmospheres except for two methods limited to narrow angle lidars the presented methods do not make any approximations, and hence do not bias the result. Nevertheless, these methods converge as quickly as any of the biasing acceleration techniques, and the probability distribution of the simulation results is almost perfectly normal. The presented variance reduction techniques have been implemented into the Monte Carlo code MYSTIC (Monte Carlo code for the physically correct tracing of photons in cloudy atmospheres) in order to validate the techniques. (C) 2010 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据