Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High-Resolution ASTER Observations

摘要

Recently, Zhang et al. (2016, https://doi.org/10.1002/2016JD024837) presented a mathematical framework based on a second-order Taylor series expansion in order to quantify the plane-parallel homogeneous bias (PPHB) in cloud optical thickness (τ) and effective droplet radius (r_(eff)) retrieved from the bispectral solar reflective method. This study provides observational validation of the aforementioned framework, using high-resolution reflectance observations from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) over 48 marine boundary layer cloud scenes. ASTER reflectances at a horizontal resolution of 30 m are aggregated up to a scale of 1,920 m, providing retrievals of τ and r_(eff) at different spatial resolutions. A comparison between the PPHB derived from these retrievals and the predicted PPHB from the mathematical framework reveals a good agreement with correlation coefficients of r >0.97 (for Δτ) and r >0.79 (for Δr_(eff)). To test the feasibility of PPHB predictions for present and future satellite missions, a scale analysis with varying horizontal resolutions of the subpixel and pixel-level observations is performed, followed by tests of corrections with only limited observational high-resolution data. It is shown that for reasonably thick clouds with a mean subpixel τ larger than 5, correlations between observed and predicted PPHB remain high, even if the number of available subpixels decreases or just a single band provides the information about subpixel reflectance variability. Only for thin clouds the predicted Δr_(eff) become less reliable, which can be attributed primarily to an increased retrieval uncertainty for r_(eff).