Validation of a Partitioning Method for Deriving VIS and NIR Fluxes from SW Satellite Observations under Clear-sky Conditions

Accurately dividing the shortwave (SW) radiation into visible (VIS) and near-infrared (NIR) components is crucial for improving climate studies, as these bands interact differently with the atmosphere and surface, influencing energy balance and feedback mechanisms. Despite its importance, no long-term data records currently exist that provide separate VIS and NIR flux measurements from satellite observations, limiting our understanding of their individual contributions to climate processes. To address this gap, NASA’s Libera mission (to be launched in 2027) will directly measure SW and NIR radiances and derive SW, VIS and NIR irradiances at the TOA using novel conversion methods. Computations of these SW sub-components will complement the data processing, both at the surface and the TOA. Preceding the direct measurement record, the partitioning of SW flux, e.g., from CERES data, into VIS and NIR components could be achieved indirectly, utilizing the relationship between SW, VIS and NIR anisotropy factors, collocated spectral imager data, and radiative transfer modeling. Preliminary results by Su et al., (2024) demonstrate a strong correlation between the spatial distribution of the indirectly derived NIR/VIS flux ratio and cloud optical depth (COD) patterns, suggesting the method's potential accuracy over the ocean. However, further validation is necessary to establish the method's robustness. This study aims to validate the indirect partitioning method using the high-spectral radiative transfer model MODTRAN6 under clear-sky conditions. MODTRAN6 will be employed to generate VIS and NIR fluxes both directly through its band model and using the partitioning method. By comparing these outputs, we will assess the accuracy of the indirect partitioning method. Successful validation would potentially enable the creation of a long-term VIS and NIR flux data record for the satellite era, enhancing our ability to study and understand climate dynamics.

References:

Su et al., (2024): https://ceres.larc.nasa.gov/documents/STM/2024-05/04_Su_CERES_STM_2024Spring.pdf