A Geant4 Monte Carlo toolkit-based radiative transfer model for studying the impact of aerosols

被引:0
|
作者
Boudjella, M. Y. [1 ]
Belbachir, A. H. [1 ]
Dib, A. S. A. [1 ]
Meftah, M. [2 ]
机构
[1] Univ Sci & Technol Oran Mohamed Boudiaf USTO MB, Fac Phys, Lab Anal & Applicat Radiat LAAR, BP 1505, Oran 31000, Algeria
[2] Univ Paris Saclay, Sorbonne Univ, Univ Versailles St Quentin En Yvelines, Lab Atmospheres Milieux Observat Spatiales LATMOS,, 11 Blvd Alembert, F-78280 Guyancourt, France
关键词
Geant4; DISORT code; Global irradiance; Diffuse irradiance; TOA reflected irradiance; Radiative forcing; SKY SOLAR IRRADIANCE; PHOTOSYNTHETICALLY ACTIVE RADIATION; LIBRADTRAN SOFTWARE PACKAGE; MULTIPLE-SCATTERING; PART I; ATMOSPHERE; CODE; PERFORMANCE; VALIDATION; VERSION;
D O I
10.1016/j.asr.2024.07.057
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Radiative transfer models are of prime importance in quantifying the earth-atmosphere energy budget. A radiative transfer model based on the Geant4 Monte Carlo toolkit was developed. This model is able to perform calculations of the spectral atmospheric transmittance, surface solar irradiance, as well as the Top of the Atmosphere (TOA) reflected irradiance over the spectral range from 280 nm to 3000 nm. Through this study, the Geant4 toolkit was used to study the effect of rural aerosols concentration on different radiative characteristics. Geant4 radiative calculations in the presence and absence of aerosols were compared to that of DISORT code. For both surface and Top of the atmosphere simulations, good agreement was observed between the two models. Specifically, under atmospheric visibility conditions of 23 km, the mean relative difference between Geant4 and DISORT code was found to be less than 2.46 % for Global irradiance calculations and less than 7.9 % for TOA reflected irradiance. A comparative analysis between the measured total global irradiance in Adrar city and the irradiance estimated by Geant4 demonstrated the general effectiveness of Geant4, with a coefficient of determination of 0.991 and a Mean Absolute Percentage Error (MAPE) of 5.851 %.<br /> (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:74 / 90
页数:17
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