Efficient Monte Carlo Methods for Radiative Transfer Modeling

Abstract

Demands for Monte Carlo radiative transfer modeling have grown with the increase in computational power in recent decades. This method provides realistic simulations of radiation processes for various types of application, including radiation budgets in cloudy conditions and remote measurements of clouds, aerosols, and gases. Despite many advantages, such as explicit treatment of three-dimensional radiative transfer, issues of numerical efficiency can make the method intractable, especially in radiance calculations. The commonly used local estimation method requires computationally intensive ray tracing at each collision. Furthermore, the realistic phase function of Mie scattering by cloud and aerosol particles has very sharp peaks in the forward direction. Radiance computations by Monte Carlo methods are inefficient for such spiky phase functions because of significant noise. Moreover, in optically thin regions, sampling of radiance contributions is so rare that long computing times are required to reduce noise. To solve these issues, several variance reduction methods have been proposed. This paper discusses a modified local estimation method, a truncation approximation for a highly anisotropic phase function, a collision-forcing method for optically thin media, a numerical diffusion technique, and several related topics. Numerical experiments demonstrated significant improvements in efficiency for solar radiance calculations in a limited number of cloudy cases.

NO<sub>2</sub> observations over the western Pacific and Indian Ocean by MAX-DOAS on <i>Kaiyo</i>, a Japanese research vessel

“…The box air mass factor (A box ), which is defined as the air mass factor for a given layer, was derived from the O 4 DSCD inversion with the Monte Carlo Atmospheric Radiative Transfer Simulator (MCARaTS) (Iwabuchi, 2006). Using the A box , we retrieved NO 2 profiles in the lower troposphere with a vertical step of 1 km from the NO 2 DSCD measurements.…”

Validation of OMI tropospheric NO<sub>2</sub> column data using MAX-DOAS measurements deep inside the North China Plain in June 2006: Mount Tai Experiment 2006

“…The aerosol retrieval was made using the optimal estimation method (Rodgers, 2000;Irie et al, 2008). A lookup table (LUT) for the vertical profile of A box , which characterizes the ratio of partial slant to vertical columns for a given layer, was created by a three-dimensional Monte Carlo radiative transfer model, MCARaTS (Iwabuchi, 2006), and used to find the optimal aerosol and A box profiles that account for O 4 SCD values measured at all ELs. MCARaTS is a parallelized threedimensional radiative transfer model utilizing the forwardpropagating Monte Carlo photon transport algorithm.…”

“…High-precision calculations of the A box profiles were made by simulating 10 6 photons. The collision-forcing method of Iwabuchi (2006) was used to reduce the computational noise in the cases of an optically thin atmosphere. The A box calculations by MCARaTS have been validated through comparisons with other radiative transfer models (Wagner et al, 2007).…”

Characterization of OMI Tropospheric NO2 Measurements in East Asia Based on a Robust Validation Comparison

“…The aerosol retrieval method has been validated against coincident aerosol measurements by lidar and sky radiometer at Tsukuba (e.g., Irie et al 2009) and Hedo (Takashima et al 2009). A lookup table for the vertical profile of the box air mass factor (Abox), which characterizes the ratio of the partial slant to vertical columns for a given layer, was created using a three-dimensional Monte Carlo radiative transfer model, MCARaTS (Iwabuchi 2006), and used to find the optimal aerosol and Abox profiles that account for O4 SCD values measured at all ELs. Abox calculations by MCARaTS have been validated through comparisons with other radiative transfer models (Wagner et al 2007).…”