A Hybrid Interlayer Reflective Boundary Approximation for Hyperspectral Cloud Radiance Simulation Under Optically Thick Liquid Cloud Conditions

Accurate simulation of hyperspectral cloud radiance remains challenging under optically thick cloud conditions, where conventional layered radiative transfer (RT) models tend to underestimate cloud-induced backscattering and return radiance in the visible to shortwave infrared (VIS–SWIR) range. In this study, we propose an extinction-dependent interlayer reflective augmentation within a Curtis–Godson (CG)-based layered RT framework. While the cloud-top and cloud-bottom heights are still used to define the cloudy layer in the radiative transfer simulation, the proposed method does not impose a single bulk reflective boundary at the cloud scale. Instead, it adds an extinction-dependent reflective coupling term at discretized sublayer interfaces to compensate for the underrepresented backward radiative contribution in standard layered solvers. The proposed approach is designed for optically thick, plane-parallel cloud conditions and aims to improve forward radiance simulation rather than detailed microphysical retrieval. The formulation is constructed so that the reflective augmentation vanishes as the local extinction decreases, although the present experiments focus on optically thick liquid-cloud cases. The numerical evaluation is conducted over the 0.8–2.5 μm range, corresponding to the valid unsaturated Gaofen-5A (GF-5A) bands used for comparison. Validation using GF-5A hyperspectral observations indicates that the proposed method improves the spectral fidelity of simulated thick-cloud radiance under the adopted representative cloud-parameter setting and scene-level anchoring strategy. Relative to the baseline compact layered RT formulation, the proposed method provides a favorable balance between computational efficiency and spectral accuracy, making it suitable as a fast forward module for hyperspectral cloud radiance simulation of optically thick liquid-cloud scenes.