Monte Carlo Simulation of Solar Reflectances for Cloudy AtmospheresSource: Journal of the Atmospheric Sciences:;2003:;Volume( 060 ):;issue: 016::page 1881DOI: 10.1175/1520-0469(2003)060<1881:MCSOSR>2.0.CO;2Publisher: American Meteorological Society
Abstract: Monte Carlo simulations of solar radiative transfer were performed for a well-resolved, large, three-dimensional (3D) domain of boundary layer cloud simulated by a cloud-resolving model. In order to represent 3D distributions of optical properties for ?2 ? 106 cloudy cells, attenuation by droplets was handled by assigning each cell a cumulative distribution of extinction derived from either a model or an assumed discrete droplet size spectrum. This minimizes the required number of detailed phase functions. Likewise, to simulate statistically significant, high-resolution imagery, it was necessary to apply variance reduction techniques. Three techniques were developed for use with the local estimation method of computing reflectance ?. First, small fractions of ? come from numerous, small contributions of ? computed at each scattering event. Terminating calculation of ? when it falls below ?min ≈ 10?3 was found to impact estimates of ? minimally but reduced computation time by ?10%. Second, large fractions of ? come from infrequent realizations of large ?. When sampled poorly, they boost Monte Carlo noise significantly. Removing ? ? ?max, storing them in a domainwide reservoir, adding ?max to local estimates of ?, and, at simulation's end, distributing the reservoir across the domain in proportion to local ?, tends to reduce variance much. This regionalization technique works well when the number of photons per unit area is small (nominally ? 50 000). A value of ?max ≈ 100 reduces variance of ? greatly with little impact on estimates of ?. Third, if ? are computed using exact (e.g., Mie) phase functions for the first N scattering events, and thereafter a blunt-nosed corresponding phase function (e.g., Henyey?Greenstein) is used, production of large ? is thwarted resulting in reduced variance and time required to achieve accurate estimates of ?.
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contributor author | Barker, H. W. | |
contributor author | Goldstein, R. K. | |
contributor author | Stevens, D. E. | |
date accessioned | 2017-06-09T14:38:14Z | |
date available | 2017-06-09T14:38:14Z | |
date copyright | 2003/08/01 | |
date issued | 2003 | |
identifier issn | 0022-4928 | |
identifier other | ams-23299.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4159844 | |
description abstract | Monte Carlo simulations of solar radiative transfer were performed for a well-resolved, large, three-dimensional (3D) domain of boundary layer cloud simulated by a cloud-resolving model. In order to represent 3D distributions of optical properties for ?2 ? 106 cloudy cells, attenuation by droplets was handled by assigning each cell a cumulative distribution of extinction derived from either a model or an assumed discrete droplet size spectrum. This minimizes the required number of detailed phase functions. Likewise, to simulate statistically significant, high-resolution imagery, it was necessary to apply variance reduction techniques. Three techniques were developed for use with the local estimation method of computing reflectance ?. First, small fractions of ? come from numerous, small contributions of ? computed at each scattering event. Terminating calculation of ? when it falls below ?min ≈ 10?3 was found to impact estimates of ? minimally but reduced computation time by ?10%. Second, large fractions of ? come from infrequent realizations of large ?. When sampled poorly, they boost Monte Carlo noise significantly. Removing ? ? ?max, storing them in a domainwide reservoir, adding ?max to local estimates of ?, and, at simulation's end, distributing the reservoir across the domain in proportion to local ?, tends to reduce variance much. This regionalization technique works well when the number of photons per unit area is small (nominally ? 50 000). A value of ?max ≈ 100 reduces variance of ? greatly with little impact on estimates of ?. Third, if ? are computed using exact (e.g., Mie) phase functions for the first N scattering events, and thereafter a blunt-nosed corresponding phase function (e.g., Henyey?Greenstein) is used, production of large ? is thwarted resulting in reduced variance and time required to achieve accurate estimates of ?. | |
publisher | American Meteorological Society | |
title | Monte Carlo Simulation of Solar Reflectances for Cloudy Atmospheres | |
type | Journal Paper | |
journal volume | 60 | |
journal issue | 16 | |
journal title | Journal of the Atmospheric Sciences | |
identifier doi | 10.1175/1520-0469(2003)060<1881:MCSOSR>2.0.CO;2 | |
journal fristpage | 1881 | |
journal lastpage | 1894 | |
tree | Journal of the Atmospheric Sciences:;2003:;Volume( 060 ):;issue: 016 | |
contenttype | Fulltext |