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Enhanced shortwave radiative transfer model based on SBDART

  • role： First author第一作者
  • Affiliation：

    Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518000, China

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：jingzhao_rs@foxmail.com
  • Introduction：赵静(1987—　)，女，博士研究生，研究方向为定量遥感。E-mail：jingzhao_rs@foxmail.com
Jing ZHAO12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：gjyan@bnu.edu.cn
  • Introduction：阎广建(1972—　)，男，教授，研究方向为多角度遥感测量、建模及参数反演。E-mail：gjyan@bnu.edu.cn
Guangjian YAN2*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Institute of Geology, China Earthquake Administration, Beijing 100029, China

Zhonghu JIAO23,  
  • Affiliation：

    Beijing Forestry University, Beijing 100083, China

Ling CHEN4,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Qing CHU2

résumé

Clouds, being the most abundant and variable factor in the atmosphere, are critical to the modification of Earth–atmosphere energy balance. The effects of clouds on radiation should therefore be carefully and thoroughly considered. However, traditional atmospheric radiative transfer models only consider two extreme situations, namely, all clear and overcast. To understand the influence of clouds on surface shortwave radiation and improve the accuracy of shortwave radiative components derived from remote sensing dataset, we propose a novel radiative transfer model to analyze the cloud conditions in this work. Based on the traditional one-dimensional radiative transfer model Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART), this study first classifies the actual sun/cloud-viewing geometric conditions into nine subtypes, considering whether the directions of the sun and sensor are obscured by clouds. Then, the original formula of land surface downward radiative components is expanded. Two cloud fraction parameters (i.e., hemispherical effective cloud fraction and regional cloud fraction) are introduced to the formula to establish an improved shortwave radiative transfer model, namely, the SBDART-CF model. Based on the formula, the nine subtypes are summarized into two types, that is, the situations where the direction of the sun is either obscured by the cloud or not. Then, the atmospheric spherical albedo and atmospheric transmission of different cloud conditions are compared to narrow the range of cloud parameters. Other thirteen parameters, such as solar zenith angle, surface albedo, and cloud thickness, among others, are introduced to the following sensitive analysis. Finally, the effects of the above-mentioned model parameters on the surface shortwave radiative components under different circumstances are calculated and analyzed by using SimLab software, which employs a global quantitative sensitivity analysis method. The proposed shortwave radiative transfer model can efficiently describe the influence of clouds on the surface shortwave radiation by considering the cloud horizontal distribution in the sky. Solar zenith angle and surface albedo both play significant roles in the modification of downward shortwave radiation and surface net shortwave radiation. Hemispherical effective cloud fraction and regional cloud fraction also considerably affect the radiation components. Six factors that are also important to the model can be easily derived from the satellite products and therefore can contribute to the model application effectively. Water vapor, ozone, and carbon dioxide column volume exert minimal effects on the surface shortwave radiation components under all the considered conditions. The above analysis results show that the proposed SBDART-CF model can deal with shortwave radiative problems under different cloud conditions. Therefore, the model can effectively accomplish the radiative component estimation from remotely sensed datasets.

mots-clés

surface shortwave radiation;cloud parameter;radiative transfer model;SBDART;SimLab;sensitivity analysis

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