A partitioning method for deriving VIS and NIR fluxes from SW observations under clear-sky conditions
Xiang Zhong1, Xiquan Dong1, Baike Xi1, Jordann Brendecke1, Jake Gristey2,3,4, Maria Hakuba5, Bruce Kindel4, Steven Massie4, Daniel Feldman6, Peter Pilewskie4,7, Norman Loeb8, Wenying Su8
1 Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
2 Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
3 NOAA Chemical Sciences Laboratory, Boulder, CO, USA
4 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO, USA
5 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
6 Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
7 Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
8 NASA Langley Research Center, Hampton, Virginia
Accurately partitioning shortwave (SW) radiation into visible (VIS) and near-infrared (NIR) components is critical for improving climate models and understanding energy balance dynamics. This study presents a novel method for deriving VIS and NIR fluxes from SW observations under clear-sky conditions. A key component of this method is the sensitivity of the partitioning process to the anisotropic factor ratio, β, which is shown to be most sensitive to solar zenith angle (SZA), view zenith angle (VZA), relative azimuth angle (RAA), and aerosol optical depth (AOD). A polynomial regression approach has been developed to retrieve β for different scenes. By combining this regressed β with radiance ratios of VIS to NIR, the method partitions the SW fluxes into VIS and NIR components. The performance of the partitioning method has been evaluated for both forest and desert surfaces using a radiative transfer model (MODTRAN6), showing good agreement with direct MODTRAN output for VIS and NIR fluxes, with deviations less than 3 W/m². The results indicate that the method's accuracy is better for backward angles compared to forward angles, with forest surfaces exhibiting more consistent and reliable partitioning compared to desert surfaces. While current study is limited to only two surface types, the sensitivity of β to key environmental variables highlights the potential for extending this method to a wider range of conditions, with future work focusing on aerosol impacts and further validation.