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

Abstract

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 .