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Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.

Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds