Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High‐Resolution ASTER Observations. Issue 8 (26th April 2018) - Werner, F.; Zhang, Z.; Wind, G.; Miller, D. J.; Platnick, S.
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 Δ τ ) and r > 0.79 (for Δ r 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 Δ r eff become less reliable, which can be attributed primarily to an increased retrieval uncertainty for r eff . Plain Language Summary: Assumptions in the retrieval of cloud optical and microphysical properties from remote sensing observations can be substantially biased. Only recently, we gained a better understanding of these biases, aided by a mathematical framework that makes use of the subpixel variability within an observed pixel. This study uses novel satellite observations at a very high resolution to provide observational validation of the proposed mathematical framework, which before was mainly tested on a single synthetic cloud field based on simulations. The presented results illustrate that the mathematical framework can reliably correct for the observed bias. Moreover, this approach also yields reasonable results when applied to more common satellite setup, which are usually characterized by lower spatial resolutions or limited availability of subpixel observations. Key Points: Observed PPHB in MBL cloud scenes can be larger than 5 for cloud optical thickness, several microns for effective droplet radius Mathematical framework can explain and correct for observed PPHB PPHB correction still yields reliable results if only a few subpixels or just a single visible band provides high‐resolution reflectances